diff --git a/README.md b/README.md
index af2d27e..d58636f 100644
--- a/README.md
+++ b/README.md
@@ -21,13 +21,13 @@ from essm_jax.essm import ExtendedStateSpaceModel
 tfpd = tfp.distributions
 
 
-def transition_fn(z, t, t_next):
+def transition_fn(z, t, t_next, *args):
     mean = z + jnp.sin(2 * jnp.pi * t / 10 * z)
     cov = 0.1 * jnp.eye(np.size(z))
     return tfpd.MultivariateNormalTriL(mean, jnp.linalg.cholesky(cov))
 
 
-def observation_fn(z, t):
+def observation_fn(z, t, *args):
     mean = z
     cov = t * 0.01 * jnp.eye(np.size(z))
     return tfpd.MultivariateNormalTriL(mean, jnp.linalg.cholesky(cov))
diff --git a/docs/examples/excitable_damped_harmonic_oscillator.ipynb b/docs/examples/excitable_damped_harmonic_oscillator.ipynb
index 7188573..2121060 100644
--- a/docs/examples/excitable_damped_harmonic_oscillator.ipynb
+++ b/docs/examples/excitable_damped_harmonic_oscillator.ipynb
@@ -2,13 +2,13 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "ef3eaff3797489e6",
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-08-14T22:28:24.816871897Z",
-     "start_time": "2024-08-14T22:28:23.676057239Z"
+     "end_time": "2024-08-17T07:31:07.544978977Z",
+     "start_time": "2024-08-17T07:31:07.212440889Z"
     }
    },
    "outputs": [],
@@ -30,8 +30,8 @@
    "source": [
     "Let's define the dampled harmonic oscillator\n",
     "\n",
-    "$$m \\ddot x= - c \\dot x - k x + f(t)$$\n",
-    "$$\\iff \\ddot x = - 2 \\zeta \\omega \\dot x - \\omega^2 x + \\frac{f(t)}{m}$$\n",
+    "$$m \\ddot x= - c \\dot x - k (x-x_0) + f(t)$$\n",
+    "$$\\iff \\ddot x = - 2 \\zeta \\omega \\dot x - \\omega^2 (x - x_0) + \\frac{f(t)}{m}$$\n",
     "\n",
     "with $\\omega = \\frac{k}{m} \\in \\mathbb{R}^+$, $\\zeta = \\frac{c}{2 \\sqrt{mk}} \\in \\mathbb{R}^+$ and $f(t) \\sim \\mathcal{N}[0, \\sigma_f^2]$.\n",
     "\n",
@@ -43,7 +43,7 @@
     "\n",
     "transition mean function,\n",
     "\n",
-    "$$ z_{t}, t \\to [x + v \\Delta t, v + (-c v - k x) \\Delta t, \\bar M]$$\n",
+    "$$ z_{t}, t \\to [x + v \\Delta t, v + \\frac{(f(t) -c v - k x)}{m} \\Delta t, \\bar M]$$\n",
     "\n",
     "transition noise scale function,\n",
     "\n",
@@ -65,18 +65,33 @@
   {
    "cell_type": "code",
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n"
+     ]
+    },
     {
      "data": {
       "text/plain": "<Figure size 640x480 with 1 Axes>",
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
     },
+    {
+     "data": {
+      "text/plain": "<matplotlib.collections.PolyCollection at 0x7f4ee8ad2650>"
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
     {
      "data": {
       "text/plain": "<Figure size 640x480 with 1 Axes>",
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -84,6 +99,9 @@
    ],
    "source": [
     "\n",
+    "from tensorflow_probability.substrates.jax.internal import special_math\n",
+    "from jax import lax\n",
+    "from jax._src.scipy.special import ndtri\n",
     "from essm_jax.pytee_utils import pytree_unravel\n",
     "from typing import NamedTuple\n",
     "\n",
@@ -94,8 +112,8 @@
     "    \"\"\"\n",
     "    x: jax.Array  # [n]\n",
     "    v: jax.Array  # [n]\n",
-    "    a: jax.Array  # [n]\n",
-    "    M: jax.Array  # [n]\n",
+    "    Zeta: jax.Array  # [n]\n",
+    "    Omega: jax.Array  # [n]\n",
     "\n",
     "\n",
     "class Observation(NamedTuple):\n",
@@ -112,22 +130,20 @@
     "class ExcitedDampedHarmonicOscillator:\n",
     "    n: int = 1\n",
     "    dt: float = 0.01\n",
+    "    x0: float = 0.\n",
     "    sigma_f: float = 0.1\n",
     "    sigma_x: float = 0.1\n",
-    "    zeta: float = 0.1\n",
-    "    omega: float = 1.\n",
-    "    m_bar: float = 1.\n",
+    "    sigma_Zeta: float = 0.01\n",
+    "    sigma_Omega: float = 0.01\n",
+    "    zeta0: float = 0.1\n",
+    "    omega0: float = 1.\n",
     "\n",
     "    def __post_init__(self):\n",
-    "        self.c = 2 * self.zeta * self.omega\n",
-    "        self.k = self.omega ** 2\n",
-    "        self.M_bar = jnp.log(self.m_bar)\n",
-    "\n",
     "        example_state = State(\n",
     "            x=jnp.ones((n,)),\n",
     "            v=jnp.ones((n,)),\n",
-    "            a=jnp.ones((n,)),\n",
-    "            M=jnp.ones((n,))\n",
+    "            Zeta=jnp.ones((n,)),\n",
+    "            Omega=jnp.ones((n,)),\n",
     "        )\n",
     "\n",
     "        example_observation = Observation(\n",
@@ -165,24 +181,43 @@
     "        # This is needed because calling unravel is only defined on the per element basis.\n",
     "        return jax.vmap(self.obs_unravel_fn)(flat_observables)\n",
     "\n",
-    "    def transition_fn(self, z: jax.Array, t: jax.Array, t_next) -> (tfpd.MultivariateNormalLinearOperator):\n",
+    "    def transition_fn(self, z: jax.Array, t: jax.Array, t_next: jax.Array, *args) -> tfpd.MultivariateNormalLinearOperator:\n",
     "        dt = t_next - t\n",
     "        state = self.state_unravel_fn(z)\n",
+    "        zeta = self.constrain_zeta_fn(state.Zeta)\n",
+    "        omega = self.constrain_omega_fn(state.Omega)\n",
+    "        c = 2. * zeta * omega\n",
+    "        k = omega ** 2\n",
+    "        a_mean = (-c * state.v - k * (state.x - self.x0))\n",
+    "        adt = a_mean * dt\n",
     "        next_state_mean = State(\n",
-    "            x=state.x + state.v * dt,\n",
-    "            v=state.v + (-self.c * state.v - self.k * state.x) * dt,\n",
-    "            a=(-self.c * state.v - self.k * state.x),\n",
-    "            M=self.M_bar * jnp.ones_like(state.M)\n",
+    "            x=state.x + state.v * dt + 0.5 * adt * dt,\n",
+    "            v=state.v + adt,\n",
+    "            Zeta=state.Zeta,\n",
+    "            Omega=state.Omega\n",
+    "        )\n",
+    "        var_f = self.sigma_f ** 2\n",
+    "        zero = jnp.zeros((self.n,))\n",
+    "        ones = jnp.ones((self.n,))\n",
+    "        # Cov divided by var_f * dt**2 +  1e-6 I\n",
+    "        cov_x = State(x=0.25 * dt ** 2 * ones + 1e-6, v=0.5 * dt * ones, Zeta=zero, Omega=zero)\n",
+    "        cov_v = State(x=0.5 * dt * ones, v=ones + 1e-6, Zeta=zero, Omega=zero)\n",
+    "        cov_Zeta = State(x=zero, v=zero, Zeta=self.sigma_Zeta ** 2 / (var_f * dt) * ones + 1e-6, Omega=zero)\n",
+    "        cov_Omega = State(x=zero, v=zero, Zeta=zero, Omega=self.sigma_Omega ** 2 / (var_f * dt) * ones + 1e-6)\n",
+    "        next_state_cov = State(\n",
+    "            x=self.state_ravel_fn(cov_x),\n",
+    "            v=self.state_ravel_fn(cov_v),\n",
+    "            Zeta=self.state_ravel_fn(cov_Zeta),\n",
+    "            Omega=self.state_ravel_fn(cov_Omega)\n",
     "        )\n",
-    "        next_state_scale = State(\n",
-    "            x=jnp.zeros_like(state.x),\n",
-    "            v=self.sigma_f * jnp.sqrt(dt) * jnp.ones_like(state.v),\n",
-    "            a=jnp.zeros_like(state.a),\n",
-    "            M=jnp.zeros_like(state.M)\n",
+    "        sqrt_small_factor = self.sigma_f * dt\n",
+    "        return tfpd.MultivariateNormalTriL(\n",
+    "            self.state_ravel_fn(next_state_mean),\n",
+    "            sqrt_small_factor * lax.linalg.cholesky(self.batched_state_ravel_fn(next_state_cov),\n",
+    "                                                    symmetrize_input=False)\n",
     "        )\n",
-    "        return tfpd.MultivariateNormalDiag(self.state_ravel_fn(next_state_mean), self.state_ravel_fn(next_state_scale))\n",
     "\n",
-    "    def observation_fn(self, z: jax.Array, t: jax.Array) -> tfpd.MultivariateNormalLinearOperator:\n",
+    "    def observation_fn(self, z: jax.Array, t: jax.Array, *args) -> tfpd.MultivariateNormalLinearOperator:\n",
     "        state = self.state_unravel_fn(z)\n",
     "        obs_mean = Observation(\n",
     "            x=state.x,\n",
@@ -192,18 +227,50 @@
     "        )\n",
     "        return tfpd.MultivariateNormalDiag(self.obs_ravel_fn(obs_mean), self.obs_ravel_fn(obs_scale))\n",
     "\n",
+    "    def constrain_zeta_fn(self, Zeta: jax.Array) -> jax.Array:\n",
+    "        concentration1 = 2 * jnp.ones((self.n,))\n",
+    "        concentration0 = 2 * jnp.ones((self.n,))\n",
+    "        zeta_dist = tfpd.Beta(concentration1=concentration1, concentration0=concentration0)\n",
+    "        u = special_math.ndtr(Zeta)\n",
+    "        zeta = zeta_dist.quantile(u)\n",
+    "        return zeta\n",
+    "\n",
+    "    def constrain_omega_fn(self, Omega: jax.Array) -> jax.Array:\n",
+    "        low = jnp.zeros((self.n,))\n",
+    "        high = 2 * jnp.pi * jnp.ones((self.n,)) * 10.\n",
+    "        omega_dist = tfpd.Uniform(low=low, high=high)\n",
+    "        u = special_math.ndtr(Omega)\n",
+    "        omega = omega_dist.quantile(u)\n",
+    "        return omega\n",
+    "\n",
+    "    def unconstrain_zeta_fn(self, zeta: jax.Array) -> jax.Array:\n",
+    "        concentration1 = 2 * jnp.ones((self.n,))\n",
+    "        concentration0 = 2 * jnp.ones((self.n,))\n",
+    "        zeta_dist = tfpd.Beta(concentration1=concentration1, concentration0=concentration0)\n",
+    "        u = zeta_dist.cdf(zeta)\n",
+    "        Zeta = ndtri(u)\n",
+    "        return Zeta\n",
+    "\n",
+    "    def unconstrain_omega_fn(self, omega: jax.Array) -> jax.Array:\n",
+    "        low = jnp.zeros((self.n,))\n",
+    "        high = 2 * jnp.pi * jnp.ones((self.n,)) * 10.\n",
+    "        omega_dist = tfpd.Uniform(low=low, high=high)\n",
+    "        u = omega_dist.cdf(omega)\n",
+    "        Omega = ndtri(u)\n",
+    "        return Omega\n",
+    "\n",
     "    def create_initial_state_prior(self) -> tfpd.MultivariateNormalLinearOperator:\n",
     "        initial_state = State(\n",
-    "            x=jnp.zeros((n,)),\n",
-    "            v=jnp.zeros((n,)),\n",
-    "            a=jnp.zeros((n,)),\n",
-    "            M=self.M_bar * jnp.ones((n,))\n",
+    "            x=0.*jnp.ones((self.n,)),\n",
+    "            v=jnp.ones((self.n,)),\n",
+    "            Zeta=self.unconstrain_zeta_fn(self.zeta0 * jnp.ones((self.n,))),\n",
+    "            Omega=self.unconstrain_omega_fn(self.omega0 * jnp.ones((self.n,))),\n",
     "        )\n",
     "        initial_state_scale = State(\n",
-    "            x=jnp.zeros((n,)),\n",
-    "            v=jnp.zeros((n,)),\n",
-    "            a=jnp.zeros((n,)),\n",
-    "            M=jnp.zeros((n,))\n",
+    "            x=jnp.ones((self.n,)),\n",
+    "            v=jnp.ones((self.n,)),\n",
+    "            Zeta=jnp.ones((self.n,)),\n",
+    "            Omega=jnp.ones((self.n,)),\n",
     "        )\n",
     "        return tfpd.MultivariateNormalDiag(self.state_ravel_fn(initial_state), self.state_ravel_fn(initial_state_scale))\n",
     "\n",
@@ -219,51 +286,103 @@
     "\n",
     "model = ExcitedDampedHarmonicOscillator(\n",
     "    n=1,\n",
-    "    dt=0.01,\n",
-    "    sigma_f=0.01,\n",
-    "    sigma_x=0.001,\n",
-    "    zeta=0.1,\n",
-    "    omega=1.,\n",
-    "    m_bar=1.\n",
+    "    dt=0.1,\n",
+    "    x0=0.,\n",
+    "    sigma_f=0.5,\n",
+    "    sigma_x=1.,\n",
+    "    sigma_Zeta=0.1,\n",
+    "    sigma_Omega=0.1,\n",
+    "    zeta0=0.1,\n",
+    "    omega0=6.\n",
     ")\n",
     "\n",
+    "T = int(10 / model.dt)\n",
+    "\n",
     "essm = model.build_essm()\n",
     "\n",
-    "samples = essm.sample(key=jax.random.PRNGKey(0), num_time=1000)\n",
+    "samples = essm.sample(key=jax.random.PRNGKey(0), num_time=T)\n",
+    "\n",
+    "filter_result = essm.forward_filter(observations=samples.observation)\n",
+    "smooth_result = essm.backward_smooth(filter_result=filter_result)\n",
+    "\n",
+    "future_samples = essm.forward_simulate(key=jax.random.PRNGKey(0), num_time=int(5 / model.dt),\n",
+    "                                       filter_result=filter_result)\n",
+    "future_latent = model.batched_state_unravel_fn(future_samples.latent)\n",
     "\n",
     "obs = model.batched_observables_unravel_fn(samples.observation)\n",
     "state = model.batched_state_unravel_fn(samples.latent)\n",
     "plt.plot(samples.t, state.x, label='x', c='r')\n",
     "plt.plot(samples.t, state.v, label='v', c='b')\n",
-    "plt.plot(samples.t, state.a, label='a', c='g')\n",
-    "plt.plot(samples.t, state.M, label='M', c='k')\n",
+    "plt.plot(samples.t, state.Zeta, label='Zeta', c='purple')\n",
+    "plt.plot(samples.t, state.Omega, label='Omega', c='orange')\n",
     "plt.plot(samples.t, obs.x, label='observed', alpha=0.5)\n",
     "\n",
-    "filter_result = essm.forward_filter(observations=samples.observation)\n",
-    "\n",
-    "future_samples = essm.forward_simulate(key=jax.random.PRNGKey(0), num_time=100, filter_result=filter_result)\n",
-    "future_latent = model.batched_state_unravel_fn(future_samples.latent)\n",
     "plt.plot(future_samples.t, future_latent.x, c='r')\n",
     "plt.plot(future_samples.t, future_latent.v, c='b')\n",
-    "plt.plot(future_samples.t, future_latent.a, c='g')\n",
-    "plt.plot(future_samples.t, future_latent.M, c='k')\n",
+    "plt.plot(future_samples.t, future_latent.Zeta, c='purple')\n",
+    "plt.plot(future_samples.t, future_latent.Omega, c='orange')\n",
+    "\n",
     "plt.legend()\n",
     "plt.show()\n",
     "\n",
     "filter_latent = model.batched_state_unravel_fn(filter_result.filtered_mean)\n",
+    "filter_latent_std = model.batched_state_unravel_fn(jnp.sqrt(jax.vmap(jnp.diag)(filter_result.filtered_cov)))\n",
+    "\n",
     "plt.plot(filter_result.t, filter_latent.x, label='x', c='r')\n",
     "plt.plot(filter_result.t, filter_latent.v, label='v', c='b')\n",
-    "plt.plot(filter_result.t, filter_latent.a, label='a', c='g')\n",
-    "plt.plot(filter_result.t, filter_latent.M, label='M', c='k')\n",
+    "plt.plot(filter_result.t, filter_latent.Zeta, label='Zeta', c='purple')\n",
+    "plt.fill_between(\n",
+    "    filter_result.t, filter_latent.Zeta[:, 0] - filter_latent_std.Zeta[:, 0],\n",
+    "                     filter_latent.Zeta[:, 0] + filter_latent_std.Zeta[:, 0],\n",
+    "    alpha=0.5\n",
+    ")\n",
+    "plt.plot(filter_result.t, filter_latent.Omega, label='Omega', c='orange')\n",
+    "plt.fill_between(\n",
+    "    filter_result.t, filter_latent.Omega[:, 0] - filter_latent_std.Omega[:, 0],\n",
+    "                     filter_latent.Omega[:, 0] + filter_latent_std.Omega[:, 0],\n",
+    "    alpha=0.5\n",
+    ")"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2024-08-17T07:31:21.336170178Z",
+     "start_time": "2024-08-17T07:31:08.877542646Z"
+    }
+   },
+   "id": "759c7e41d608eb0b",
+   "execution_count": 3
+  },
+  {
+   "cell_type": "code",
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_1254184/980648694.py:10: UserWarning: No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n",
+      "  plt.legend()\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 640x480 with 1 Axes>",
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
     "\n",
     "filter_state = essm.create_filter_state(filter_result=filter_result)\n",
-    "for _ in range(100):\n",
+    "for _ in range(int(5 / model.dt)):\n",
     "    filter_state = essm.incremental_predict(filter_state=filter_state)\n",
     "    latent = model.state_unravel_fn(filter_state.filtered_mean)\n",
     "    plt.scatter(filter_state.t, latent.x, c='r', s=1)\n",
     "    plt.scatter(filter_state.t, latent.v, c='b', s=1)\n",
-    "    plt.scatter(filter_state.t, latent.a, c='g', s=1)\n",
-    "    plt.scatter(filter_state.t, latent.M, c='k', s=1)\n",
+    "    plt.scatter(filter_state.t, latent.Zeta, c='purple', s=1)\n",
+    "    plt.scatter(filter_state.t, latent.Omega, c='orange', s=1)\n",
     "\n",
     "plt.legend()\n",
     "plt.show()\n",
@@ -272,8 +391,8 @@
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-08-14T22:31:00.060073207Z",
-     "start_time": "2024-08-14T22:30:53.098775507Z"
+     "end_time": "2024-08-17T07:32:21.067217482Z",
+     "start_time": "2024-08-17T07:31:21.340748408Z"
     }
    },
    "id": "5f0b000d8cfe6d2b",
diff --git a/docs/examples/online_filtering.ipynb b/docs/examples/online_filtering.ipynb
deleted file mode 100644
index 0658db6..0000000
--- a/docs/examples/online_filtering.ipynb
+++ /dev/null
@@ -1,196 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "ef3eaff3797489e6",
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2024-08-14T22:35:52.089737670Z",
-     "start_time": "2024-08-14T22:35:51.100189045Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import jax\n",
-    "import jax.numpy as jnp\n",
-    "import numpy as np\n",
-    "import tensorflow_probability.substrates.jax as tfp\n",
-    "import pylab as plt\n",
-    "\n",
-    "from essm_jax.essm import ExtendedStateSpaceModel\n",
-    "\n",
-    "tfpd = tfp.distributions\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "Let's define a non-linear transition function that forces the state proportionally to its magnitude,\n",
-    "\n",
-    "$$p(z_{t+1} | z_t, t) = \\mathcal{N}[T(z_t, t), \\sigma_t^2]$$\n",
-    "\n",
-    "with,\n",
-    "\n",
-    "$$T(z_t, t) = z_t \\left(1 + |z_t| \\sin \\left(2 \\pi \\frac{t}{10}\\right)\\right)$$\n",
-    "\n",
-    "and \n",
-    "\n",
-    "$$\\sigma_t = 0.1$$\n",
-    "\n",
-    "Now for the observation function, let's define also a non-linear one, that takes the absolute value.\n",
-    "\n",
-    "$$p(x_t | z_t, t) = \\mathcal{N}[O(z_t, t), \\epsilon_t^2]$$\n",
-    "\n",
-    "with \n",
-    "\n",
-    "$$O(z_t, t) = |z_t|$$\n",
-    "\n",
-    "and noise that oscillates with time,\n",
-    "\n",
-    "$$\\epsilon_t = 0.01 + 0.1 \\cos\\left(2\\pi \\frac{t}{5}\\right)$$\n"
-   ],
-   "metadata": {
-    "collapsed": false
-   },
-   "id": "716c02a70cfecdff"
-  },
-  {
-   "cell_type": "code",
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": "<Figure size 640x480 with 1 Axes>",
-      "image/png": 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"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "\n",
-    "\n",
-    "def transition_fn(z, t, t_next):\n",
-    "    mean = z * (1 + jnp.abs(z) * jnp.sin(2 * jnp.pi * t / 10))\n",
-    "    scale = 0.01 * jnp.ones(np.size(z))\n",
-    "    return tfpd.MultivariateNormalDiag(mean, scale)\n",
-    "\n",
-    "\n",
-    "def observation_fn(z, t):\n",
-    "    mean = jnp.abs(z)\n",
-    "    scale = 0.001 * jnp.ones(np.size(z)) + 0.01 * (2 * jnp.pi * t / 5.)\n",
-    "    return tfpd.MultivariateNormalDiag(mean, scale)\n",
-    "\n",
-    "\n",
-    "n = 1\n",
-    "\n",
-    "initial_state_prior = tfpd.MultivariateNormalDiag(jnp.zeros(n), jnp.ones(n))\n",
-    "\n",
-    "essm = ExtendedStateSpaceModel(\n",
-    "    transition_fn=transition_fn,\n",
-    "    observation_fn=observation_fn,\n",
-    "    initial_state_prior=initial_state_prior,\n",
-    "    more_data_than_params=False,  # if observation is bigger than latent we can speed it up.\n",
-    "    dt=1.\n",
-    ")\n",
-    "samples = essm.sample(jax.random.PRNGKey(0), 100)\n",
-    "\n",
-    "plt.plot(samples.t, samples.observation)\n",
-    "plt.show()"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2024-08-14T22:35:52.766401446Z",
-     "start_time": "2024-08-14T22:35:52.094140163Z"
-    }
-   },
-   "id": "5f0b000d8cfe6d2b",
-   "execution_count": 2
-  },
-  {
-   "cell_type": "code",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "<Figure size 640x480 with 1 Axes>",
-      "image/png": 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"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "\n",
-    "filter_result = essm.forward_filter(samples.observation)\n",
-    "\n",
-    "filter_state = essm.create_initial_filter_state()\n",
-    "\n",
-    "\n",
-    "for i in range(100):\n",
-    "    filter_state = essm.incremental_predict(filter_state)\n",
-    "    # incorperate new data\n",
-    "    filter_state, _ = essm.incremental_update(filter_state, samples.observation[i])\n",
-    "    \n",
-    "    plt.scatter(filter_state.t, filter_state.filtered_mean, c='black')\n",
-    "    \n",
-    "\n",
-    "plt.plot(filter_result.t, filter_result.filtered_mean[:, 0], label='filtered latent')\n",
-    "plt.legend()\n",
-    "plt.show()"
-   ],
-   "metadata": {
-    "collapsed": true,
-    "ExecuteTime": {
-     "end_time": "2024-08-14T22:35:58.070899470Z",
-     "start_time": "2024-08-14T22:35:52.776945243Z"
-    }
-   },
-   "id": "initial_id",
-   "execution_count": 3
-  },
-  {
-   "cell_type": "code",
-   "outputs": [],
-   "source": [],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2024-08-14T22:35:58.071206840Z",
-     "start_time": "2024-08-14T22:35:58.066994695Z"
-    }
-   },
-   "id": "6626b28fc500ba6d",
-   "execution_count": 3
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/docs/examples/ou_process.ipynb b/docs/examples/ou_process.ipynb
new file mode 100644
index 0000000..55920f1
--- /dev/null
+++ b/docs/examples/ou_process.ipynb
@@ -0,0 +1,541 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "id": "ef3eaff3797489e6",
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2024-11-05T18:36:22.835135Z",
+     "start_time": "2024-11-05T18:36:21.087473Z"
+    }
+   },
+   "source": [
+    "import tensorflow_probability.substrates.jax as tfp\n",
+    "\n",
+    "tfpd = tfp.distributions\n",
+    "tfpk = tfp.math.psd_kernels\n",
+    "\n",
+    "from jaxns import marginalise_dynamic\n",
+    "from jax import random\n",
+    "from jax import numpy as jnp\n",
+    "import pylab as plt\n",
+    "import numpy as np\n",
+    "\n"
+   ],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/albert/miniconda3/envs/dsa_py/lib/python3.11/site-packages/jaxns/internals/mixed_precision.py:14: UserWarning: JAX x64 is not enabled. Setting it now. Check for errors.\n",
+      "  warnings.warn(\"JAX x64 is not enabled. Setting it now. Check for errors.\")\n",
+      "INFO:jax._src.xla_bridge:Unable to initialize backend 'cuda': \n",
+      "INFO:jax._src.xla_bridge:Unable to initialize backend 'rocm': module 'jaxlib.xla_extension' has no attribute 'GpuAllocatorConfig'\n",
+      "INFO:jax._src.xla_bridge:Unable to initialize backend 'tpu': INTERNAL: Failed to open libtpu.so: libtpu.so: cannot open shared object file: No such file or directory\n",
+      "WARNING:jax._src.xla_bridge:An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n"
+     ]
+    }
+   ],
+   "execution_count": 1
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# OU process\n",
+    "\n",
+    "The SDE for the Ornstein-Uhlenbeck process, with long-term mean, is given by\n",
+    "\n",
+    "$$\n",
+    "dx = \\frac{1}{\\tau} (\\mu - x) dt + \\sigma dW\n",
+    "$$\n",
+    "\n",
+    "The equivalent GP kernel is given by\n",
+    "\n",
+    "$$\n",
+    "k(x, x') = \\frac{\\sigma^2 \\tau}{2} \\exp\\left(-\\frac{|x - x'|}{\\tau}\\right)\n",
+    "$$"
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "716c02a70cfecdff"
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "N = 100\n",
+    "num_outliers = int(0.15 * N)\n",
+    "np.random.seed(42)\n",
+    "X = jnp.linspace(-2., 2., N)[:, None]\n",
+    "dt = X[1, 0] - X[0, 0]\n",
+    "true_mu, true_tau, true_sigma = 0., 0.2, np.sqrt(0.2)\n",
+    "true_scale = true_tau\n",
+    "true_amplitude = true_sigma * jnp.sqrt(true_tau / 2.)\n",
+    "true_noise = 0.1 * true_amplitude\n",
+    "data_mu = jnp.zeros((N,))\n",
+    "prior_cov = tfpk.MaternOneHalf(amplitude=true_amplitude, length_scale=true_scale).matrix(X, X) + 1e-13 * jnp.eye(N)\n",
+    "Y = true_mu + jnp.linalg.cholesky(prior_cov) @ random.normal(random.PRNGKey(42), shape=(N,)) + data_mu\n",
+    "Y_obs = Y + true_noise * random.normal(random.PRNGKey(1), shape=(N,))\n",
+    "plt.plot(X[:, 0], Y, c='red', label='underlying')\n",
+    "plt.scatter(X[:, 0], Y_obs, c='cyan', label='data')\n",
+    "plt.legend()\n",
+    "plt.show()\n"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2024-11-05T18:36:24.093281Z",
+     "start_time": "2024-11-05T18:36:22.849100Z"
+    }
+   },
+   "id": "759c7e41d608eb0b",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 2
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "\n",
+    "from essm_jax.essm import ExtendedStateSpaceModel\n",
+    "\n",
+    "\n",
+    "def transition_fn(z, t, t_next, map_estimate):\n",
+    "    dt = t_next - t\n",
+    "    sigma = map_estimate['sigma']\n",
+    "    tau = map_estimate['tau']\n",
+    "    mu = map_estimate['mu']\n",
+    "    x = z\n",
+    "\n",
+    "    x_mean = x + (mu - x) / tau * dt\n",
+    "    x_std = sigma * jnp.sqrt(dt)\n",
+    "\n",
+    "    return tfpd.MultivariateNormalDiag(loc=x_mean, scale_diag=x_std * jnp.ones_like(x))\n",
+    "\n",
+    "\n",
+    "def observation_fn(z, t, map_estimate):\n",
+    "    x = z\n",
+    "    uncert = map_estimate['uncert']\n",
+    "    return tfpd.MultivariateNormalDiag(loc=x, scale_diag=uncert * jnp.ones_like(x))\n",
+    "\n",
+    "\n",
+    "initial_state_prior = tfpd.MultivariateNormalDiag(loc=jnp.zeros((1,)), scale_diag=jnp.ones((1,)))\n",
+    "\n",
+    "essm = ExtendedStateSpaceModel(\n",
+    "    transition_fn=transition_fn,\n",
+    "    observation_fn=observation_fn,\n",
+    "    initial_state_prior=initial_state_prior,\n",
+    "    dt=dt / 10\n",
+    ")\n",
+    "\n",
+    "samples = essm.sample(\n",
+    "    random.PRNGKey(42), N * 10, {'tau': true_tau, 'sigma': true_sigma, 'mu': true_mu, 'uncert': true_noise},\n",
+    ")\n",
+    "plt.plot(samples.t, samples.latent[:, 0], c='green', label='ESSM')\n",
+    "\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2024-11-05T18:36:25.116133Z",
+     "start_time": "2024-11-05T18:36:24.222381Z"
+    }
+   },
+   "id": "69aa689141513d67",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 3
+  },
+  {
+   "cell_type": "code",
+   "source": [],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2024-11-05T18:36:25.190642Z",
+     "start_time": "2024-11-05T18:36:25.188667Z"
+    }
+   },
+   "id": "9fc409f14e6cc058",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "\n",
+    "\n",
+    "import jax\n",
+    "from jaxns import Prior, Model, NestedSampler\n",
+    "\n",
+    "kernel = tfpk.MaternOneHalf\n",
+    "\n",
+    "# X should be sorted\n",
+    "y_mean = jnp.mean(Y_obs)\n",
+    "y_std = jnp.std(Y_obs)\n",
+    "rmsd = jnp.sqrt(jnp.mean(jnp.square(jnp.diff(Y_obs, axis=0))))\n",
+    "mean_dt = jnp.mean(jnp.diff(X, axis=0))\n",
+    "min_tau = mean_dt * 3\n",
+    "max_tau = 0.5 * (jnp.max(X) - jnp.min(X))\n",
+    "\n",
+    "\n",
+    "def log_normal(x, mean, cov):\n",
+    "    L = jnp.linalg.cholesky(cov)\n",
+    "    return tfpd.MultivariateNormalTriL(mean, L).log_prob(x)\n",
+    "\n",
+    "\n",
+    "def log_likelihood(uncert, tau, sigma, mu):\n",
+    "    \"\"\"\n",
+    "    P(Y|sigma, half_width) = N[Y, f, K]\n",
+    "    Args:\n",
+    "        sigma:\n",
+    "        l:\n",
+    "\n",
+    "    Returns:\n",
+    "\n",
+    "    \"\"\"\n",
+    "    amplitude = jnp.sqrt(sigma ** 2 * tau / 2)\n",
+    "    scale = tau\n",
+    "    K = kernel(amplitude=amplitude, length_scale=scale).matrix(X, X)\n",
+    "    data_cov = jnp.square(uncert) * jnp.eye(X.shape[0])\n",
+    "    return log_normal(Y_obs, mu, K + data_cov)\n",
+    "\n",
+    "\n",
+    "def predict_f(uncert, tau, sigma, mu):\n",
+    "    amplitude = jnp.sqrt(sigma ** 2 * tau / 2)\n",
+    "    scale = tau\n",
+    "    K = kernel(amplitude=amplitude, length_scale=scale).matrix(X, X)\n",
+    "    data_cov = jnp.square(uncert) * jnp.eye(X.shape[0])\n",
+    "    return mu + K @ jnp.linalg.solve(K + data_cov, Y_obs - mu)\n",
+    "\n",
+    "\n",
+    "def predict_fvar(uncert, tau, sigma, mu):\n",
+    "    amplitude = jnp.sqrt(sigma ** 2 * tau / 2)\n",
+    "    scale = tau\n",
+    "    K = kernel(amplitude=amplitude, length_scale=scale).matrix(X, X)\n",
+    "    data_cov = jnp.square(uncert) * jnp.eye(X.shape[0])\n",
+    "    return jnp.diag(K - K @ jnp.linalg.solve(K + data_cov, K))\n",
+    "\n",
+    "\n",
+    "# Build the model\n",
+    "\n",
+    "def prior_model():\n",
+    "    tau = yield Prior(tfpd.Uniform(min_tau, max_tau), name='tau')\n",
+    "    uncert = yield Prior(tfpd.Uniform(true_noise * 0.1, true_noise * 10.), name='uncert')\n",
+    "    # amplitude = jnp.sqrt(sigma ** 2 * tau / 2)\n",
+    "    # sigma = sqrt(2 / tau) * amplitude\n",
+    "    mean_sigma = jnp.sqrt(2. / tau) * y_std / jnp.sqrt(dt)  # if dX is sigma dW then rms(dX/dt) = sigma\n",
+    "    sigma = yield Prior(tfpd.Exponential(1. / mean_sigma), name='sigma')\n",
+    "    mu = yield Prior(tfpd.Normal(y_mean, y_std), name='mu')\n",
+    "    return uncert, tau, sigma, mu\n",
+    "\n",
+    "\n",
+    "model = Model(prior_model=prior_model, log_likelihood=log_likelihood)\n",
+    "\n",
+    "model.sanity_check(random.PRNGKey(0), S=100)\n",
+    "\n",
+    "# Create the nested sampler class. In this case without any tuning.\n",
+    "exact_ns = NestedSampler(model=model)\n",
+    "\n",
+    "termination_reason, state = jax.jit(exact_ns)(random.PRNGKey(42))\n",
+    "results = exact_ns.to_results(termination_reason=termination_reason, state=state)\n",
+    "\n",
+    "exact_ns.summary(results)\n",
+    "exact_ns.plot_diagnostics(results)\n",
+    "exact_ns.plot_cornerplot(results)\n",
+    "\n",
+    "predict_f = marginalise_dynamic(random.PRNGKey(42), results.samples, results.log_dp_mean,\n",
+    "                                results.ESS, predict_f)\n",
+    "\n",
+    "predict_fvar = marginalise_dynamic(random.PRNGKey(42), results.samples, results.log_dp_mean,\n",
+    "                                   results.ESS, predict_fvar)\n",
+    "\n",
+    "plt.scatter(X[:, 0], Y_obs, label='data')\n",
+    "plt.plot(X[:, 0], Y, label='underlying')\n",
+    "plt.plot(X[:, 0], predict_f, label='marginalised')\n",
+    "plt.plot(X[:, 0], predict_f + jnp.sqrt(predict_fvar), ls='dotted',\n",
+    "         c='black')\n",
+    "plt.plot(X[:, 0], predict_f - jnp.sqrt(predict_fvar), ls='dotted',\n",
+    "         c='black')\n",
+    "plt.title(\"Kernel: {}\".format(kernel.__class__.__name__))\n",
+    "plt.legend()\n",
+    "plt.show()\n",
+    "\n",
+    "logZ_m12, logZerr_m12 = results.log_Z_mean, results.log_Z_uncert"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2024-11-05T18:37:11.115492Z",
+     "start_time": "2024-11-05T18:36:25.212688Z"
+    }
+   },
+   "id": "6c2a1119e646668e",
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:jaxns:Sanity check...\n",
+      "INFO:jaxns:Sanity check passed\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--------\n",
+      "Termination Conditions:\n",
+      "Small remaining evidence\n",
+      "--------\n",
+      "likelihood evals: 79181\n",
+      "samples: 1800\n",
+      "phantom samples: 0\n",
+      "likelihood evals / sample: 44.0\n",
+      "phantom fraction (%): 0.0%\n",
+      "--------\n",
+      "logZ=105.87 +- 0.24\n",
+      "max(logL)=114.55\n",
+      "H=-5.17\n",
+      "ESS=332\n",
+      "--------\n",
+      "mu: mean +- std.dev. | 10%ile / 50%ile / 90%ile | MAP est. | max(L) est.\n",
+      "mu: -0.033 +- 0.058 | -0.107 / -0.032 / 0.028 | -0.036 | -0.037\n",
+      "--------\n",
+      "sigma: mean +- std.dev. | 10%ile / 50%ile / 90%ile | MAP est. | max(L) est.\n",
+      "sigma: 0.397 +- 0.046 | 0.339 / 0.398 / 0.455 | 0.44 | 0.444\n",
+      "--------\n",
+      "tau: mean +- std.dev. | 10%ile / 50%ile / 90%ile | MAP est. | max(L) est.\n",
+      "tau: 0.42 +- 0.37 | 0.14 / 0.24 / 0.98 | 0.13 | 0.13\n",
+      "--------\n",
+      "uncert: mean +- std.dev. | 10%ile / 50%ile / 90%ile | MAP est. | max(L) est.\n",
+      "uncert: 0.021 +- 0.012 | 0.005 / 0.02 / 0.037 | 0.001 | 0.001\n",
+      "--------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/albert/miniconda3/envs/dsa_py/lib/python3.11/site-packages/jaxns/plotting.py:45: UserWarning: Found samples with zero likelihood evaluations.\n",
+      "  warnings.warn(\"Found samples with zero likelihood evaluations.\")\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 800x1200 with 5 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 800x800 with 10 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 4
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "from typing import Dict\n",
+    "from jaxns import maximum_a_posteriori_point\n",
+    "\n",
+    "map_estimate: Dict[str, jax.Array] = maximum_a_posteriori_point(results=results)\n",
+    "print(map_estimate)\n",
+    "map_estimate['tau'] = true_tau\n",
+    "map_estimate['sigma'] = true_sigma\n",
+    "map_estimate['mu'] = true_mu\n",
+    "map_estimate['uncert'] = true_noise\n",
+    "print(map_estimate)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2024-11-05T18:37:11.156574Z",
+     "start_time": "2024-11-05T18:37:11.150977Z"
+    }
+   },
+   "id": "9cd0ff3407cdb64b",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'mu': Array(-0.03576323, dtype=float64), 'sigma': Array(0.44039283, dtype=float64), 'tau': Array(0.13172916, dtype=float64), 'uncert': Array(0.00141641, dtype=float64)}\n",
+      "{'mu': 0.0, 'sigma': 0.4472135954999579, 'tau': 0.2, 'uncert': Array(0.01414214, dtype=float64)}\n"
+     ]
+    }
+   ],
+   "execution_count": 5
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "from essm_jax.essm import ExtendedStateSpaceModel\n",
+    "\n",
+    "\n",
+    "def transition_fn(z, t, t_next, map_estimate):\n",
+    "    dt = t_next - t\n",
+    "    sigma = map_estimate['sigma']\n",
+    "    tau = map_estimate['tau']\n",
+    "    mu = map_estimate['mu']\n",
+    "    x = z\n",
+    "\n",
+    "    x_mean = x + (mu - x) / tau * dt\n",
+    "    x_std = sigma * jnp.sqrt(dt)\n",
+    "    return tfpd.MultivariateNormalDiag(loc=x_mean, scale_diag=x_std * jnp.ones_like(x))\n",
+    "\n",
+    "\n",
+    "def observation_fn(z, t, map_estimate):\n",
+    "    x = z\n",
+    "    uncert = map_estimate['uncert']\n",
+    "    return tfpd.MultivariateNormalDiag(loc=x, scale_diag=uncert * jnp.ones_like(x))\n",
+    "\n",
+    "\n",
+    "initial_state_prior = tfpd.MultivariateNormalDiag(loc=jnp.zeros((1,)), scale_diag=jnp.ones((1,)))\n",
+    "\n",
+    "essm = ExtendedStateSpaceModel(\n",
+    "    transition_fn=transition_fn,\n",
+    "    observation_fn=observation_fn,\n",
+    "    initial_state_prior=initial_state_prior,\n",
+    "    dt=mean_dt\n",
+    ")"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2024-11-05T18:37:11.207402Z",
+     "start_time": "2024-11-05T18:37:11.197426Z"
+    }
+   },
+   "id": "9629382ab0916de9",
+   "outputs": [],
+   "execution_count": 6
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "# plt.plot(X[:, 0], Y, c='red', label='underlying')\n",
+    "plt.scatter(X[:, 0], Y_obs, c='cyan', label='data')\n",
+    "filter_state = essm.create_initial_filter_state(t0=jnp.min(X) - essm.dt)\n",
+    "for i in range(N):\n",
+    "    filter_state = essm.incremental_predict(filter_state, map_estimate)\n",
+    "    x_prior = filter_state.filtered_mean\n",
+    "    filter_state, _ = essm.incremental_update(filter_state, Y_obs[i], map_estimate)\n",
+    "    x_update = filter_state.filtered_mean\n",
+    "    # plt.scatter(filter_state.t, x_prior, c='blue')\n",
+    "    plt.scatter(filter_state.t, x_update, c='red')\n",
+    "    plt.scatter(filter_state.t, x_update + np.sqrt(filter_state.filtered_cov), c='black', marker='x', s=1)\n",
+    "    plt.scatter(filter_state.t, x_update - np.sqrt(filter_state.filtered_cov), c='black', marker='x', s=1)\n",
+    "\n",
+    "samples = essm.forward_simulate(\n",
+    "    random.PRNGKey(42), N, filter_state, map_estimate\n",
+    ")\n",
+    "plt.plot(samples.t, samples.latent[:, 0], c='green', label='ESSM latent')\n",
+    "\n",
+    "plt.legend()\n",
+    "plt.show()\n",
+    "\n"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2024-11-05T19:02:18.168900Z",
+     "start_time": "2024-11-05T19:02:10.895670Z"
+    }
+   },
+   "id": "5f0b000d8cfe6d2b",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 12
+  },
+  {
+   "cell_type": "code",
+   "source": [],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2024-11-05T18:37:20.888534Z",
+     "start_time": "2024-11-05T18:37:20.886498Z"
+    }
+   },
+   "id": "18947ba51062df7b",
+   "outputs": [],
+   "execution_count": null
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/essm_jax/jvp_op.py b/essm_jax/jvp_op.py
deleted file mode 100644
index 4f9be78..0000000
--- a/essm_jax/jvp_op.py
+++ /dev/null
@@ -1,175 +0,0 @@
-import dataclasses
-import warnings
-from typing import Callable, Any, Optional
-
-import jax
-import jax.numpy as jnp
-import numpy as np
-
-
-def isinstance_namedtuple(obj) -> bool:
-    """
-    Check if object is a namedtuple.
-
-    Args:
-        obj: object
-
-    Returns:
-        bool
-    """
-    return (
-            isinstance(obj, tuple) and
-            hasattr(obj, '_asdict') and
-            hasattr(obj, '_fields')
-    )
-
-
-@dataclasses.dataclass(eq=False)
-class JVPLinearOp:
-    """
-    Represents J_ij = d/d x_j f_i(x), where x is the primal value.
-
-    This is a linear operator that represents the Jacobian of a function.
-    """
-    fn: Callable  # A function R^n -> R^m
-    primals: Optional[Any] = None  # The primal value, i.e. where jacobian is evaluated
-    more_outputs_than_inputs: bool = False  # If True, the operator is tall, i.e. m > n
-    adjoint: bool = False  # If True, the operator is transposed
-    promote_dtypes: bool = False  # If True, promote dtypes to match primal during JVP, and cotangent to match primal_out during VJP
-
-    def __post_init__(self):
-        if not callable(self.fn):
-            raise ValueError('`fn` must be a callable.')
-
-        if isinstance_namedtuple(self.primals) or (not isinstance(self.primals, (tuple, list))):
-            self.primals = (self.primals,)
-
-    def __call__(self, *primals: Any) -> 'JVPLinearOp':
-        return JVPLinearOp(fn=self.fn, primals=primals, more_outputs_than_inputs=self.more_outputs_than_inputs,
-                           adjoint=self.adjoint, promote_dtypes=self.promote_dtypes)
-
-    def __neg__(self):
-        return JVPLinearOp(fn=lambda *args, **kwargs: -self.fn(*args, **kwargs), primals=self.primals,
-                           more_outputs_than_inputs=self.more_outputs_than_inputs, adjoint=self.adjoint)
-
-    def __matmul__(self, other):
-        if not isinstance(other, (jax.Array, np.ndarray)):
-            raise ValueError(
-                'Dunder methods currently only defined for operation on arrays. '
-                'Use .matmul(...) for general tangents.'
-            )
-        if len(np.shape(other)) == 1:
-            return self.matvec(other, adjoint=self.adjoint)
-        return self.matmul(other, adjoint=self.adjoint, left_multiply=True)
-
-    def __rmatmul__(self, other):
-        if not isinstance(other, (jax.Array, np.ndarray)):
-            raise ValueError(
-                'Dunder methods currently only defined for operation on arrays. '
-                'Use .matmul(..., left_multiply=False) for general tangents.'
-            )
-        if len(np.shape(other)) == 1:
-            return self.matvec(other, adjoint=not self.adjoint)
-        return self.matmul(other, adjoint=not self.adjoint, left_multiply=False)
-
-    @property
-    def T(self) -> 'JVPLinearOp':
-        return JVPLinearOp(fn=self.fn, primals=self.primals, more_outputs_than_inputs=self.more_outputs_than_inputs,
-                           adjoint=not self.adjoint)
-
-    def matmul(self, *tangents: Any, adjoint: bool = False, left_multiply: bool = True):
-        """
-        Implements matrix multiplication from matvec using vmap.
-
-        Args:
-            tangents: pytree of the same structure as the primals, but with appropriate more columns for adjoint=False,
-                or more rows for adjoint=True.
-            adjoint: if True, compute J.T @ v, else compute J @ v
-            left_multiply: if True, compute M @ J, else compute J @ M
-
-        Returns:
-            pytree of matching either f-space (output) or x-space (primals)
-        """
-        if left_multiply:
-            # J.T @ M or J @ M
-            in_axes = -1
-            out_axes = -1
-        else:
-            # M @ J.T or M @ J
-            in_axes = 0
-            out_axes = 0
-        if adjoint:
-            return jax.vmap(lambda *_tangent: self.matvec(*_tangent, adjoint=adjoint),
-                            in_axes=in_axes, out_axes=out_axes)(*tangents)
-        return jax.vmap(lambda *_tangent: self.matvec(*_tangent, adjoint=adjoint),
-                        in_axes=in_axes, out_axes=out_axes)(*tangents)
-
-    def matvec(self, *tangents: Any, adjoint: bool = False):
-        """
-        Compute J @ v = sum_j(J_ij * v_j) using a JVP, if adjoint is False.
-        Compute J.T @ v = sum_i(v_i * J_ij) using a VJP, if adjoint is True.
-
-        Args:
-            tangents: pytree of the same structure as the primals.
-            adjoint: if True, compute v @ J, else compute J @ v
-
-        Returns:
-            pytree of matching either f-space (output) or x-space (primals)
-        """
-        if self.primals is None:
-            raise ValueError("The primal value must be set to compute the Jacobian.")
-
-        if adjoint:
-            co_tangents = tangents
-
-            def _adjoint_promote_dtypes(primal_out: jax.Array, co_tangent: jax.Array):
-                if co_tangent.dtype != primal_out.dtype:
-                    warnings.warn(f"Promoting co-tangent dtype from {co_tangent.dtype} to {primal_out.dtype}.")
-                return primal_out, co_tangent.astype(primal_out.dtype)
-
-            # v @ J
-            primals_out, f_vjp = jax.vjp(self.fn, *self.primals)
-            if isinstance_namedtuple(primals_out) or (not isinstance(primals_out, (tuple, list))):
-                # JAX squeezed structure to a single element, as the function only returns one output
-                co_tangents = co_tangents[0]
-                if self.promote_dtypes:
-                    primals_out, co_tangents = jax.tree_map(_adjoint_promote_dtypes, primals_out, co_tangents)
-            else:
-                if self.promote_dtypes:
-                    primals_out, co_tangents = zip(*jax.tree_map(_adjoint_promote_dtypes, primals_out, co_tangents))
-            del primals_out
-            output = f_vjp(co_tangents)
-            if len(output) == 1:
-                return output[0]
-            return output
-
-        def _promote_dtypes(primal: jax.Array, tangent: jax.Array):
-            dtype = jnp.result_type(primal, tangent)
-            if primal.dtype != dtype:
-                warnings.warn(f"Promoting primal dtype from {primal.dtype} to {dtype}.")
-            if tangent.dtype != dtype:
-                warnings.warn(f"Promoting tangent dtype from {tangent.dtype} to {dtype}.")
-            return primal.astype(dtype), tangent.astype(dtype)
-
-        primals = self.primals
-        if self.promote_dtypes:
-            primals, tangents = zip(*jax.tree_map(_promote_dtypes, primals, tangents))
-        primal_out, tangent_out = jax.jvp(self.fn, primals, tangents)
-        return tangent_out
-
-    def to_dense(self) -> jax.Array:
-        """
-        Compute the dense Jacobian at a point.
-
-        Args:
-            x: [n] array
-
-        Returns:
-            [m, n] array
-        """
-        if self.primals is None:
-            raise ValueError("The primal value must be set to compute the Jacobian.")
-
-        if self.more_outputs_than_inputs:
-            return jax.jacfwd(self.fn)(*self.primals)
-        return jax.jacrev(self.fn)(*self.primals)
diff --git a/essm_jax/sparse.py b/essm_jax/sparse.py
deleted file mode 100644
index 4b83001..0000000
--- a/essm_jax/sparse.py
+++ /dev/null
@@ -1,68 +0,0 @@
-from typing import Tuple, NamedTuple
-
-import jax
-import jax.numpy as jnp
-import numpy as np
-
-
-class SparseRepresentation(NamedTuple):
-    shape: Tuple[int, ...]
-    rows: jax.Array
-    cols: jax.Array
-    vals: jax.Array
-
-
-def create_sparse_rep(m: np.ndarray) -> SparseRepresentation:
-    """
-    Creates a sparse rep from matrix m. Use in linear models with materialise_jacobian=False for 2x speed up.
-
-    Args:
-        m: [N,M] matrix
-
-    Returns:
-        sparse rep
-    """
-    rows, cols = np.where(m)
-    sort_indices = np.lexsort((cols, rows))
-    rows = rows[sort_indices]
-    cols = cols[sort_indices]
-    return SparseRepresentation(
-        shape=np.shape(m),
-        rows=jnp.asarray(rows),
-        cols=jnp.asarray(cols),
-        vals=jnp.asarray(m[rows, cols])
-    )
-
-
-def to_dense(m: SparseRepresentation, out: jax.Array | None = None) -> jax.Array:
-    """
-    Form dense matrix.
-
-    Args:
-        m: sparse rep
-        out: output buffer
-
-    Returns:
-        out + M
-    """
-    if out is None:
-        out = jnp.zeros(m.shape, m.vals.dtype)
-
-    return out.at[m.rows, m.cols].add(m.vals, unique_indices=True, indices_are_sorted=True)
-
-
-def matvec_sparse(m: SparseRepresentation, v: jax.Array, out: jax.Array | None = None) -> jax.Array:
-    """
-    Compute matmul for sparse rep. Speeds up large sparse linear models by about 2x.
-
-    Args:
-        m: sparse rep
-        v: vec
-        out: output buffer to add to.
-
-    Returns:
-        out + M @ v
-    """
-    if out is None:
-        out = jnp.zeros(m.shape[0])
-    return out.at[m.rows].add(m.vals * v[m.cols], unique_indices=True, indices_are_sorted=True)
diff --git a/essm_jax/tests/test_sparse.py b/essm_jax/tests/test_sparse.py
deleted file mode 100644
index 32ef1c2..0000000
--- a/essm_jax/tests/test_sparse.py
+++ /dev/null
@@ -1,31 +0,0 @@
-import jax
-import numpy as np
-from jax import numpy as jnp
-
-jax.config.update('jax_enable_x64', True)
-
-from essm_jax.sparse import create_sparse_rep, matvec_sparse, to_dense
-
-
-def test_sparse_rep():
-    m = np.asarray([[1., 0, 0],
-                    [-1., 2., 0.],
-                    [0., 0., 5.]])
-    rep = create_sparse_rep(m)
-    v = jnp.asarray([1, 1, 1])
-    np.testing.assert_allclose(matvec_sparse(rep, v), m @ v)
-
-    m = np.random.normal(size=(100, 100))
-    v = np.random.normal(size=100)
-
-    rep = create_sparse_rep(m)
-    np.testing.assert_allclose(matvec_sparse(rep, v), m @ v)
-
-
-def test_to_dense():
-    m = np.asarray([[1., 0, 0],
-                    [-1., 2., 0.],
-                    [0., 0., 5.]])
-    rep = create_sparse_rep(m)
-    M = to_dense(rep)
-    np.testing.assert_allclose(M, m)
diff --git a/pyproject.toml b/pyproject.toml
index b5a3c46..9cf8582 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,32 @@
+# pyproject.toml
+
 [build-system]
-requires = [
-    "setuptools>=42",
-    "wheel"
+requires = ["setuptools>=61.0", "wheel"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "essm_jax"
+version = "1.0.2"
+description = "Extended State Space Modelling in JAX"
+readme = "README.md"
+requires-python = ">3.9"
+license = { text = "Apache Software License" }
+authors = [{ name = "Joshua G. Albert", email = "albert@strw.leidenuniv.nl" }]
+keywords = ["kalman", "non-linear", "EKF", "modelling"]
+classifiers = [
+    "Programming Language :: Python :: 3",
+    "License :: OSI Approved :: Apache Software License",
+    "Operating System :: OS Independent"
 ]
-build-backend = "setuptools.build_meta"
\ No newline at end of file
+urls = { "Homepage" = "https://github.com/joshuaalbert/essm_jax" }
+
+[project.optional-dependencies]
+# Define the extras here; they will be loaded dynamically from setup.py
+examples = []  # Placeholders; extras will load from setup.py
+
+[tool.setuptools]
+include-package-data = true
+
+
+[tool.setuptools.packages.find]
+where = ["src"]
diff --git a/requirements-examples.txt b/requirements-examples.txt
index 3230e66..771df10 100644
--- a/requirements-examples.txt
+++ b/requirements-examples.txt
@@ -1,2 +1,3 @@
 matplotlib
-jupyter
\ No newline at end of file
+jupyter
+jaxns
\ No newline at end of file
diff --git a/setup.py b/setup.py
index bf6d268..7d40cf7 100755
--- a/setup.py
+++ b/setup.py
@@ -1,36 +1,17 @@
 #!/usr/bin/env python
 
-from setuptools import find_packages
 from setuptools import setup
 
-install_requires = [
-    'jax',
-    'jaxlib',
-    'numpy<2',
-    'tensorflow_probability'
-]
 
-with open("README.md", "r") as fh:
-    long_description = fh.read()
+def load_requirements(file_name):
+    with open(file_name, "r") as file:
+        return [line.strip() for line in file if line.strip() and not line.startswith("#")]
 
-setup(name='essm_jax',
-      version='1.0.1',
-      description='Extended State Spapce Model in JAX',
-      long_description=long_description,
-      long_description_content_type="text/markdown",
-      url="https://github.com/joshuaalbert/essm_jax",
-      author='Joshua G. Albert',
-      author_email='albert@strw.leidenuniv.nl',
-      install_requires=install_requires,
-      tests_require=[
-          'pytest>=2.8',
-      ],
-      package_dir={'': './'},
-      packages=find_packages('./'),
-      classifiers=[
-          "Programming Language :: Python :: 3",
-          "License :: OSI Approved :: Apache Software License",
-          "Operating System :: OS Independent",
-      ],
-      python_requires='>3.9',
-      )
+
+setup(
+    install_requires=load_requirements("requirements.txt"),
+    extras_require={
+        "examples": load_requirements("requirements-examples.txt"),
+    },
+    tests_require=load_requirements("requirements-tests.txt"),
+)
diff --git a/essm_jax/__init__.py b/src/essm_jax/__init__.py
similarity index 100%
rename from essm_jax/__init__.py
rename to src/essm_jax/__init__.py
diff --git a/src/essm_jax/dual_essm.py b/src/essm_jax/dual_essm.py
new file mode 100644
index 0000000..662b64c
--- /dev/null
+++ b/src/essm_jax/dual_essm.py
@@ -0,0 +1,86 @@
+import dataclasses
+from typing import NamedTuple, Optional, Tuple
+
+import jax
+import tensorflow_probability.substrates.jax as tfp
+
+from essm_jax.essm import ExtendedStateSpaceModel, IncrementalFilterState
+
+tfpd = tfp.distributions
+
+__all__ = [
+    'DualExtendedStateSpaceModel',
+    'DualIncrementalFilterState'
+]
+
+
+class DualIncrementalFilterState(NamedTuple):
+    state_filter_state: IncrementalFilterState
+    param_filter_state: IncrementalFilterState
+
+
+@dataclasses.dataclass(eq=False)
+class DualExtendedStateSpaceModel:
+    """
+    Dual extended state space model for simulatanous state and parameter estimation, in an online setup. For iterative
+    estimation, the normal ESSM can be used effectively.
+
+    Args:
+        state_essm: the state ESSM, should accept the parameter as an input
+        param_essm: the parameter ESSM, should accept the state as an input
+    """
+    state_essm: ExtendedStateSpaceModel
+    param_essm: ExtendedStateSpaceModel
+
+    def create_initial_filter_state(self, t0: jax.Array | float = 0.0) -> DualIncrementalFilterState:
+        return DualIncrementalFilterState(
+            state_filter_state=self.state_essm.create_initial_filter_state(t0),
+            param_filter_state=self.param_essm.create_initial_filter_state(t0)
+        )
+
+    def incremental_update(self, filter_state: DualIncrementalFilterState, observation: jax.Array,
+                           mask: Optional[jax.Array] = None) -> Tuple[
+        DualIncrementalFilterState, tfpd.MultivariateNormalLinearOperator]:
+        """
+        Incremental update of the dual ESSM.
+
+        Args:
+            filter_state: the current filter state
+            observation: the observation at time t
+            mask: the mask for the observation
+
+        Returns:
+            the updated filter state and the marginal distribution
+        """
+        state_filter_state, marginal_dist = self.state_essm.incremental_update(
+            filter_state.state_filter_state,
+            observation,
+            filter_state.param_filter_state.filtered_mean,
+            mask=mask
+        )
+        param_filter_state, _ = self.param_essm.incremental_update(
+            filter_state.param_filter_state,
+            observation,
+            state_filter_state.filtered_mean,
+            mask=mask
+        )
+        return (
+            DualIncrementalFilterState(state_filter_state=state_filter_state, param_filter_state=param_filter_state),
+            marginal_dist
+        )
+
+    def incremental_predict(self, filter_state: DualIncrementalFilterState) -> DualIncrementalFilterState:
+        """
+        Incremental prediction of the dual ESSM.
+
+        Args:
+            filter_state: the current filter state
+
+        Returns:
+            the updated filter state
+        """
+        state_theta = filter_state.param_filter_state.filtered_mean
+        param_theta = filter_state.state_filter_state.filtered_mean
+        state_filter_state = self.state_essm.incremental_predict(filter_state.state_filter_state, state_theta)
+        param_filter_state = self.param_essm.incremental_predict(filter_state.param_filter_state, param_theta)
+        return DualIncrementalFilterState(state_filter_state=state_filter_state, param_filter_state=param_filter_state)
diff --git a/essm_jax/essm.py b/src/essm_jax/essm.py
similarity index 69%
rename from essm_jax/essm.py
rename to src/essm_jax/essm.py
index 93ba669..10f9b6f 100644
--- a/essm_jax/essm.py
+++ b/src/essm_jax/essm.py
@@ -1,13 +1,14 @@
 """Extended Gaussian State Space Model."""
 
 import dataclasses
-from typing import Callable, NamedTuple, Tuple, Union, Optional
+from typing import Callable, NamedTuple, Tuple, Union, Optional, Any
 
 import jax
 import jax.numpy as jnp
 import numpy as np
 import tensorflow_probability.substrates.jax as tfp
 from jax import lax
+from tensorflow_probability.python.internal.backend.jax.gen.linear_operator_diag import LinearOperatorDiag
 from tensorflow_probability.substrates.jax.math import hpsd_solve
 
 from essm_jax.jvp_op import JVPLinearOp
@@ -15,6 +16,11 @@
 tfpd = tfp.distributions
 tfb = tfp.bijectors
 
+__all__ = [
+    'ExtendedStateSpaceModel',
+    'IncrementalFilterState'
+]
+
 
 class SampleResult(NamedTuple):
     t: jax.Array  # [num_timesteps] The time indices
@@ -59,7 +65,7 @@ class InitialPrior(NamedTuple):
     covariance: jax.Array  # [latent_size, latent_size] The covariance of the initial state
 
 
-def _efficient_add_scalar_diag(A: jax.Array, c: Union[jax.Array, float]) -> jax.Array:
+def _efficient_add_diag(A: jax.Array, c: Union[jax.Array, float]) -> jax.Array:
     """
     Efficiently add a scalar to the diagonal of a matrix.
 
@@ -78,6 +84,28 @@ def _efficient_add_scalar_diag(A: jax.Array, c: Union[jax.Array, float]) -> jax.
     return A.at[jnp.diag_indices(n)].add(c, indices_are_sorted=True, unique_indices=True)
 
 
+def _check_shapes(observations: jax.Array, mask: Optional[jax.Array] = None):
+    """
+    Check the shapes of the observations and mask.
+
+    Args:
+        observations: [num_time, observation_size] array of observations
+        mask: [num_time] array of masks, True for missing observations
+
+    Raises:
+        ValueError: If the shapes are incorrect.
+    """
+
+    if len(observations.shape) != 2:
+        raise ValueError('observations must be a 2D array, of shape [num_time, observation_size]')
+
+    if mask is not None:
+        if len(mask.shape) != 1:
+            raise ValueError('mask must be a 1D array, of shape [num_time]')
+        if mask.shape[0] != observations.shape[0]:
+            raise ValueError('mask and observations must have the same length')
+
+
 @dataclasses.dataclass(eq=False)
 class ExtendedStateSpaceModel:
     """Extended State Space Model.
@@ -87,20 +115,20 @@ class ExtendedStateSpaceModel:
 
     Args:
         transition_fn: A function that computes the state transition distribution
-            p(z(t'), t' | z(t), t). Must return a MultivariateNormalLinearOperator.
-            Call signature is `transition_fn(z(t), t, t')`, where z(t) is the previous state.
+            p(z(t') | z(t), t, t', theta). Must return a MultivariateNormalLinearOperator.
+            Call signature is `transition_fn(z(t), t, t', *theta)`, where z(t) is the previous state.
         observation_fn: A function that computes the observation distribution
-            p(x(t) | z(t), t). Must return a MultivariateNormalLinearOperator.
-            Call signature is `observation_fn(z(t), t)`, where z(t) is the current state.
+            p(x(t) | z(t), t, theta). Must return a MultivariateNormalLinearOperator.
+            Call signature is `observation_fn(z(t), t, *theta)`, where z(t) is the current state.
             Note: t is the observation time, with t=t0 being the initial state.
         initial_state_prior: A distribution over the initial state p(z(t0)).
             Must be a MultivariateNormalLinearOperator.
         more_data_than_params: If True, the observation function has more outputs than inputs.
         materialise_jacobians: If True, the Jacobians are materialised as dense matrices.
-        dt: The time step size, default is 1. t[i] = t0 + (i+1) * dt
+        dt: The time step size, default is 1, and in general the output t[i] = t0 + (i+1) * dt
     """
-    transition_fn: Callable[[jax.Array, jax.Array, jax.Array], tfpd.MultivariateNormalLinearOperator]
-    observation_fn: Callable[[jax.Array, jax.Array], tfpd.MultivariateNormalLinearOperator]
+    transition_fn: Callable[[jax.Array, jax.Array, jax.Array, Any], tfpd.MultivariateNormalLinearOperator]
+    observation_fn: Callable[[jax.Array, jax.Array, Any], tfpd.MultivariateNormalLinearOperator]
     initial_state_prior: tfpd.MultivariateNormalLinearOperator
     more_data_than_params: bool = False
     materialise_jacobians: bool = False
@@ -119,36 +147,85 @@ def __post_init__(self):
         self.latent_size = np.size(_initial_state_prior_mean)
         self.latent_shape = np.shape(_initial_state_prior_mean)
 
-    def get_transition_jacobian(self, t: jax.Array, t_next: jax.Array) -> JVPLinearOp:
+    def get_transition_jacobian(self, t: jax.Array, t_next: jax.Array, *theta: Any) -> JVPLinearOp:
+        """
+        Get the Jacobian of the transition function.
+
+        Args:
+            t: the current time
+            t_next: the next time
+            *theta: the parameters
+
+        Returns:
+            A JVPLinearOp instance representing the Jacobian of the transition function.
+        """
+
         def _transition_fn(z):
-            return self.transition_fn(z, t, t_next).mean()
+            return self.transition_fn(z, t, t_next, *theta).mean()
 
         return JVPLinearOp(_transition_fn, more_outputs_than_inputs=False)
 
-    def get_observation_jacobian(self, t: jax.Array, observation_size: Optional[int] = None) -> JVPLinearOp:
+    def get_observation_jacobian(self, t: jax.Array, *theta: Any,
+                                 observation_size: Optional[int] = None) -> JVPLinearOp:
+        """
+        Get the Jacobian of the observation function.
+
+        Args:
+            t: the current time
+            *theta: the parameters
+            observation_size: the size of the observation, if different from the latent size
+
+        Returns:
+            A JVPLinearOp instance representing the Jacobian of the observation function.
+        """
+
         def _observation_fn(z):
-            return self.observation_fn(z, t).mean()
+            return self.observation_fn(z, t, *theta).mean()
 
         more_data_than_params = self.more_data_than_params
         if observation_size is not None:
             more_data_than_params = self.latent_size < observation_size
         return JVPLinearOp(_observation_fn, more_outputs_than_inputs=more_data_than_params)
 
-    def transition_matrix(self, z, t, t_next):
-        Fop = self.get_transition_jacobian(t, t_next)
+    def transition_matrix(self, z, t, t_next, *theta):
+        """
+        Compute the transition matrix of the linearised transition function.
+
+        Args:
+            z: the current state
+            t: the current time
+            t_next: the next time
+            *theta: the parameters
+
+        Returns:
+            [latent_size, latent_size] array
+        """
+        Fop = self.get_transition_jacobian(t, t_next, *theta)
         return Fop(z).to_dense()
 
-    def observation_matrix(self, z, t):
-        Hop = self.get_observation_jacobian(t)
+    def observation_matrix(self, z, t, *theta):
+        """
+        Compute the observation matrix of the linearised observation function.
+
+        Args:
+            z: the current state
+            t: the current time
+            *theta: the parameters
+
+        Returns:
+            [observation_size, latent_size] array
+        """
+        Hop = self.get_observation_jacobian(t, *theta)
         return Hop(z).to_dense()
 
-    def sample(self, key, num_time: int, t0: Union[jax.Array, float] = 0.) -> SampleResult:
+    def sample(self, key, num_time: int, *theta, t0: Union[jax.Array, float] = 0.) -> SampleResult:
         """
         Sample from the model.
 
         Args:
             key: a PRNGKey
             num_time: the number of time steps to sample
+            *theta: the parameters
             t0: the time of initial state
 
         Returns:
@@ -164,9 +241,9 @@ def sample(self, key, num_time: int, t0: Union[jax.Array, float] = 0.) -> Sample
         def _sample_latents_op(latent, y):
             (key, t, t_next) = y
             new_latent_key, obs_key = jax.random.split(key, 2)
-            transition_dist = self.transition_fn(latent, t, t_next)
+            transition_dist = self.transition_fn(latent, t, t_next, *theta)
             new_latent = transition_dist.sample(seed=new_latent_key)
-            observation_dist = self.observation_fn(new_latent, t)
+            observation_dist = self.observation_fn(new_latent, t, *theta)
             new_observation = observation_dist.sample(seed=obs_key)
             return new_latent, SampleResult(t=t_next, latent=new_latent, observation=new_observation)
 
@@ -187,29 +264,8 @@ def _sample_latents_op(latent, y):
 
         return samples
 
-    def _check_shapes(self, observations: jax.Array, mask: Optional[jax.Array] = None):
-        """
-        Check the shapes of the observations and mask.
-
-        Args:
-            observations: [num_time, observation_size] array of observations
-            mask: [num_time] array of masks, True for missing observations
-
-        Raises:
-            ValueError: If the shapes are incorrect.
-        """
-
-        if len(observations.shape) != 2:
-            raise ValueError('observations must be a 2D array, of shape [num_time, observation_size]')
-
-        if mask is not None:
-            if len(mask.shape) != 1:
-                raise ValueError('mask must be a 1D array, of shape [num_time]')
-            if mask.shape[0] != observations.shape[0]:
-                raise ValueError('mask and observations must have the same length')
-
     def forward_simulate(self, key: jax.Array, num_time: int,
-                         filter_result: Union[FilterResult | IncrementalFilterState]) -> SampleResult:
+                         filter_result: Union[FilterResult | IncrementalFilterState], *theta) -> SampleResult:
         """
         Simulate from the model, from the end of a forward filtering pass.
 
@@ -217,6 +273,7 @@ def forward_simulate(self, key: jax.Array, num_time: int,
             key: a PRNGKey
             num_time: the number of time steps to simulate
             filter_result: the result of the forward filtering pass, or incremental filter update.
+            *theta: the parameters
 
         Returns:
             sample result: num_time timesteps of forward simulated
@@ -224,14 +281,14 @@ def forward_simulate(self, key: jax.Array, num_time: int,
         if isinstance(filter_result, FilterResult):
             initial_state_prior = tfpd.MultivariateNormalTriL(
                 loc=filter_result.filtered_mean[-1],
-                scale_tril=lax.linalg.cholesky(_efficient_add_scalar_diag(filter_result.filtered_cov[-1], 1e-6),
+                scale_tril=lax.linalg.cholesky(_efficient_add_diag(filter_result.filtered_cov[-1], 1e-6),
                                                symmetrize_input=False)
             )
             t0 = filter_result.t[-1]
         elif isinstance(filter_result, IncrementalFilterState):
             initial_state_prior = tfpd.MultivariateNormalTriL(
                 loc=filter_result.filtered_mean,
-                scale_tril=lax.linalg.cholesky(_efficient_add_scalar_diag(filter_result.filtered_cov, 1e-6),
+                scale_tril=lax.linalg.cholesky(_efficient_add_diag(filter_result.filtered_cov, 1e-6),
                                                symmetrize_input=False)
             )
             t0 = filter_result.t
@@ -245,37 +302,44 @@ def forward_simulate(self, key: jax.Array, num_time: int,
             materialise_jacobians=self.materialise_jacobians,
             dt=self.dt
         )
-        return new_essm.sample(key=key, num_time=num_time, t0=t0)
+        return new_essm.sample(key, num_time, *theta, t0=t0)
 
-    def incremental_update(self, filter_state: IncrementalFilterState, observation: jax.Array,
+    def incremental_update(self, filter_state: IncrementalFilterState, observation: jax.Array, *theta,
                            mask: Optional[jax.Array] = None) -> Tuple[
         IncrementalFilterState, tfpd.MultivariateNormalLinearOperator]:
         """
         Perform an incremental update of the filter state. Does not advance the time index. I.e. produces
 
-        p(z[t] | x[:t]) from p(z[t] | x[:t-1]) and p(x[t] | x[:t-1])
+        p(z[t] | x[:t], theta) from p(z[t] | x[:t-1], theta) and p(x[t] | x[:t-1], theta)
 
         Args:
             filter_state: the current filter state
             observation: [n] the observation at the current time
+            *theta: the parameters
             mask: scalar, the mask at the current time, True for missing observations
 
         Returns:
             the updated filter state, and the marginal distribution of the observation p(x[t] | x[:t-1])
         """
 
-        Hop = self.get_observation_jacobian(t=filter_state.t, observation_size=observation.size)
+        Hop = self.get_observation_jacobian(filter_state.t, *theta, observation_size=observation.size)
         H = Hop(filter_state.filtered_mean)
         if self.materialise_jacobians:
             H = H.to_dense()
 
         # Update step, compute p(z[t] | x[:t]) from p(z[t] | x[:t-1])
-        observation_dist = self.observation_fn(filter_state.filtered_mean, filter_state.t)
-        R = observation_dist.covariance()
+        observation_dist = self.observation_fn(filter_state.filtered_mean, filter_state.t, *theta)
         # Push-forward the prior (i.e. predictive) distribution to the observation space
         x_expectation = observation_dist.mean()  # [observation_size]
         tmp_H_P = H @ filter_state.filtered_cov
-        S = tmp_H_P @ H.T + R  # [observation_size, observation_size]
+        _S = tmp_H_P @ H.T  # [observation_size, observation_size]
+        if isinstance(observation_dist.scale, LinearOperatorDiag):
+            R_diag = observation_dist.scale.diag_part()  # [observation_size]
+            S = _efficient_add_diag(_S, R_diag)  # [observation_size, observation_size]
+        else:
+            R = observation_dist.covariance()
+            S = _S + R  # [observation_size, observation_size]
+
         S_chol = lax.linalg.cholesky(S, symmetrize_input=False)  # [observation_size, observation_size]
 
         marginal_dist = tfpd.MultivariateNormalTriL(x_expectation, S_chol)
@@ -294,10 +358,19 @@ def incremental_update(self, filter_state: IncrementalFilterState, observation:
         # Update the state covariance using Joseph's form to ensure positive semi-definite
         # tmp_factor = (I - K @ H)
         if self.more_data_than_params:
-            tmp_factor = _efficient_add_scalar_diag((- K) @ H, 1.)
+            tmp_factor = _efficient_add_diag((- K) @ H, 1.)
         else:
-            tmp_factor = _efficient_add_scalar_diag(K @ (-H), 1.)
-        filtered_cov = tmp_factor @ filter_state.filtered_cov @ tmp_factor.T + K @ R @ K.T  # [latent_size, latent_size]
+            tmp_factor = _efficient_add_diag(K @ (-H), 1.)
+
+        _filtered_cov = tmp_factor @ filter_state.filtered_cov @ tmp_factor.T  # [latent_size, latent_size]
+        if isinstance(observation_dist.scale, LinearOperatorDiag):
+            R_diag = observation_dist.scale.diag_part()  # [observation_size]
+            # _other_part = K @ R @ K.T = (K * diag(R)) @ K.T (efficiently computed)
+            _other_part = (K * R_diag) @ K.T  # [latent_size, latent_size]
+            filtered_cov = _filtered_cov + _other_part
+        else:
+            R = observation_dist.covariance()
+            filtered_cov = _filtered_cov + K @ R @ K.T  # [latent_size, latent_size]
 
         # When masked, then the filtered state is the predicted state.
         if mask is not None:
@@ -317,31 +390,38 @@ def incremental_update(self, filter_state: IncrementalFilterState, observation:
             filtered_cov=filtered_cov
         ), marginal_dist
 
-    def incremental_predict(self, filter_state: IncrementalFilterState) -> IncrementalFilterState:
+    def incremental_predict(self, filter_state: IncrementalFilterState, *theta, t_next: jax.Array | float | None = None) -> IncrementalFilterState:
         """
         Perform an incremental prediction step of the filter state, advancing the time index. I.e. produces
 
-        p(z[t+1] | x[:t]) from p(z[t] | x[:t])
+        p(z[t+1] | x[:t]) from p(z[t] | x[:t], theta)
 
         Args:
             filter_state: the current filter state
+            *theta: the parameters
 
         Returns:
             the predicted filter state, with time index advanced
         """
-        # Predict step, compute p(z[t+1] | x[:t])
+        # Predict step, compute p(z[t+1] | x[:t], theta)
 
-        t_next = filter_state.t + jnp.asarray(self.dt, filter_state.t.dtype)
+        if t_next is None:
+            t_next = filter_state.t + jnp.asarray(self.dt, filter_state.t.dtype)
 
-        Fop = self.get_transition_jacobian(t=filter_state.t, t_next=t_next)
+        Fop = self.get_transition_jacobian(filter_state.t, t_next, *theta)
         F = Fop(filter_state.filtered_mean)
         if self.materialise_jacobians:
             F = F.to_dense()
 
-        predicted_dist = self.transition_fn(filter_state.filtered_mean, filter_state.t, t_next)
+        predicted_dist = self.transition_fn(filter_state.filtered_mean, filter_state.t, t_next, *theta)
         predicted_mean = predicted_dist.mean()  # [latent_size]
-        Q = predicted_dist.covariance()  # [latent_size, latent_size]
-        predicted_cov = F @ filter_state.filtered_cov @ F.T + Q  # [latent_size, latent_size]
+        _predicted_cov = F @ filter_state.filtered_cov @ F.T  # [latent_size, latent_size]
+        if isinstance(predicted_dist.scale, LinearOperatorDiag):
+            Q_diag = predicted_dist.scale.diag_part()
+            predicted_cov = _efficient_add_diag(_predicted_cov, Q_diag)  # [latent_size, latent_size]
+        else:
+            Q = predicted_dist.covariance()  # [latent_size, latent_size]
+            predicted_cov = _predicted_cov + Q  # [latent_size, latent_size]
 
         return IncrementalFilterState(
             t=t_next,
@@ -350,29 +430,13 @@ def incremental_predict(self, filter_state: IncrementalFilterState) -> Increment
             filtered_cov=predicted_cov
         )
 
-    def create_filter_state(self, filter_result: FilterResult) -> IncrementalFilterState:
-        """
-        Create an incremental filter state from a filter result.
-
-        Args:
-            filter_result: the filter result
-
-        Returns:
-            the incremental filter state
-        """
-        return IncrementalFilterState(
-            t=filter_result.t[-1],
-            log_cumulative_marginal_likelihood=filter_result.log_cumulative_marginal_likelihood[-1],
-            filtered_mean=filter_result.filtered_mean[-1],
-            filtered_cov=filter_result.filtered_cov[-1]
-        )
-
     def create_initial_filter_state(self, t0: Union[jax.Array, float] = 0.) -> IncrementalFilterState:
         """
         Create an incremental filter at the initial time.
 
         Args:
-            t0: the time of prior state (before the first observation)
+            t0: the time of prior state (before the first observation), default is 0. To set properly, subtract `dt`
+                from the first observation time.
 
         Returns:
             the initial incremental filter state at the first possible observation time, i.e. t0+1.
@@ -386,29 +450,30 @@ def create_initial_filter_state(self, t0: Union[jax.Array, float] = 0.) -> Incre
             filtered_cov=self.initial_state_prior.covariance()
         )
 
-    def forward_filter(self, observations: jax.Array, mask: Optional[jax.Array] = None,
+    def forward_filter(self, observations: jax.Array, *theta, mask: Optional[jax.Array] = None,
                        marginal_likelihood_only: bool = False,
                        t0: Union[jax.Array, float] = 0.) -> Union[FilterResult, jax.Array]:
         """
         Run the forward filtering pass, computing the total marginal likelihood
 
-        p(x) = prod_t p(x[t] | x[:t-1])
+        p(x) = prod_t p(x[t] | x[:t-1], theta)
 
         filtered latent distribution at each timestep,
 
-        p(z[t] | x[:t])
+        p(z[t] | x[:t], theta)
 
         Args:
             observations: [num_time, observation_size] array of observations
+            *theta: the parameters
             mask: [num_time] array of masks, True for missing observations
             marginal_likelihood_only: if True only the marginal likelihood is returned
-            t0: the time of initial state
+            t0: the time of initial state, default is 0, so first observation is at time `dt`.
 
         Returns:
             If `marginal_likelihood_only` is True, the log marginal likelihood of the observations.
             Otherwise, a `FilterResult` instance, with [num_time] batched arrays.
         """
-        self._check_shapes(observations=observations, mask=mask)
+        _check_shapes(observations=observations, mask=mask)
 
         class YType(NamedTuple):
             observation: jax.Array  # [observation_size] observation at time t
@@ -425,13 +490,14 @@ def _filter_op(filter_state: IncrementalFilterState, y: YType) -> Tuple[Incremen
             # This is so that the filter results naturally align with the smoothing operation.
 
             updated_filter_state, marginal_dist = self.incremental_update(
-                filter_state=filter_state,
-                observation=y.observation,
+                filter_state,
+                y.observation,
+                *theta,
                 mask=y.mask if mask is not None else None
             )
 
             # Predict step, compute p(z[t+1] | x[:t])
-            predicted_filter_state = self.incremental_predict(filter_state=updated_filter_state)
+            predicted_filter_state = self.incremental_predict(updated_filter_state, *theta)
 
             return predicted_filter_state, FilterResult(
                 t=updated_filter_state.t,
@@ -445,7 +511,7 @@ def _filter_op(filter_state: IncrementalFilterState, y: YType) -> Tuple[Incremen
             )
 
         filter_state = self.create_initial_filter_state(t0=t0)
-        filter_state = self.incremental_predict(filter_state)  # Advance to first update time
+        filter_state = self.incremental_predict(filter_state, *theta)  # Advance to first update time
 
         if mask is None:
             _mask = jnp.zeros(num_time, dtype=jnp.bool_)  # dummy variable (we skip the mask select)
@@ -464,43 +530,31 @@ def _filter_op(filter_state: IncrementalFilterState, y: YType) -> Tuple[Incremen
             return final_accumulate.log_cumulative_marginal_likelihood
         return filter_results
 
-    def log_prob(self, observations: jax.Array, mask: Optional[jax.Array] = None) -> jax.Array:
-        """
-        Compute the log probability of the observations under the model.
-
-        Args:
-            observations: [num_time, observation_size] array of observations
-            mask: [num_time] array of masks, True for missing observations
-
-        Returns:
-            [num_time] array of log probabilities
-        """
-        return self.forward_filter(observations, mask, marginal_likelihood_only=True)
-
-    def posterior_marginals(self, observations: jax.Array, mask: Optional[jax.Array] = None,
-                            t0: Union[jax.Array, float] = 0.) -> Union[
-        SmoothingResult, Tuple[SmoothingResult, InitialPrior]]:
+    def log_prob(self, observations: jax.Array, *theta, mask: Optional[jax.Array] = None,
+                 t0: jax.Array | float = 0.) -> jax.Array:
         """
-        Compute the posterior marginal distributions of the latents, p(z[t] | x[:T]).
+        Compute the log probability of the observations under the model, p(x[:T] | theta).
 
         Args:
             observations: [num_time, observation_size] array of observations
+            *theta: the parameters
             mask: [num_time] array of masks, True for missing observations
             t0: the time of initial state
 
         Returns:
-            A `SmoothingResult` instance, with [num_time] batched arrays.
+            [num_time] array of log probabilities
         """
-        filter_result = self.forward_filter(observations, mask, t0=t0)
-        return self.backward_smooth(filter_result, include_prior=False)
+        return self.forward_filter(observations, *theta, mask=mask, marginal_likelihood_only=True, t0=t0)
 
-    def backward_smooth(self, filter_result: FilterResult, include_prior: bool = False) -> Union[
+    def backward_smooth(self, filter_result: FilterResult, *theta, include_prior: bool = False) -> Union[
         SmoothingResult, Tuple[SmoothingResult, InitialPrior]]:
         """
-        Run the backward smoothing pass.
+        Run the backward smoothing pass, computing the smoothed latent distribution at each timestep,
+        p(z[t] | x[:T], theta).
 
         Args:
             filter_result: A `FilterResult` instance, with [num_time] batched arrays.
+            *theta: the parameters
             include_prior: if True, include the prior p(z0) in the smoothing pass.
 
         Returns:
@@ -528,14 +582,14 @@ def _smooth_op(carry: Carry, x: XType) -> Tuple[Carry, SmoothingResult]:
             """
             A single step of the backward equations.
             """
-            Fop = self.get_transition_jacobian(t=x.t - self.dt, t_next=x.t)
+            Fop = self.get_transition_jacobian(x.t - self.dt, x.t, *theta)
             F = Fop(x.filtered_mean)
             if self.materialise_jacobians:
                 F = F.to_dense()
 
             # Compute the C
             # J = y.filtered_cov @ F.T @ jnp.linalg.inv(y.predicted_cov)
-            predicted_cov_chol = lax.linalg.cholesky(_efficient_add_scalar_diag(x.predicted_cov, 1e-6),
+            predicted_cov_chol = lax.linalg.cholesky(_efficient_add_diag(x.predicted_cov, 1e-6),
                                                      symmetrize_input=False)  # Possibly need to add a small diagonal jitter
             tmp_F_P = F @ x.filtered_cov
             J = hpsd_solve(x.predicted_cov, tmp_F_P, cholesky_matrix=predicted_cov_chol).T
@@ -547,15 +601,22 @@ def _smooth_op(carry: Carry, x: XType) -> Tuple[Carry, SmoothingResult]:
             smoothed_cov = x.filtered_cov + J @ (carry.smoothed_cov - x.predicted_cov) @ J.T
 
             # Push-forward the smoothed distribution to the observation space
-            observation_dist = self.observation_fn(smoothed_mean, x.t)
-            R = observation_dist.covariance()
+            observation_dist = self.observation_fn(smoothed_mean, x.t, *theta)
 
-            Hop = self.get_observation_jacobian(t=x.t, observation_size=np.shape(filter_result.observation_mean)[1])
+            Hop = self.get_observation_jacobian(x.t, *theta,
+                                                observation_size=np.shape(filter_result.observation_mean)[1])
             H = Hop(smoothed_mean)
             if self.materialise_jacobians:
                 H = H.to_dense()
             smoothed_obs_mean = observation_dist.mean()
-            smoothed_obs_cov = H @ smoothed_cov @ H.T + R
+
+            _smoothed_obs_cov = H @ smoothed_cov @ H.T
+            if isinstance(observation_dist.scale, LinearOperatorDiag):
+                R_diag = observation_dist.scale.diag_part()
+                smoothed_obs_cov = _efficient_add_diag(_smoothed_obs_cov, R_diag)
+            else:
+                R = observation_dist.covariance()
+                smoothed_obs_cov = _smoothed_obs_cov + R
 
             return Carry(
                 smoothed_mean=smoothed_mean,
@@ -579,7 +640,7 @@ def _smooth_op(carry: Carry, x: XType) -> Tuple[Carry, SmoothingResult]:
             # prepend the initial state
             t0 = xs.t[0] - self.dt
             init_filter_state = self.create_initial_filter_state(t0=t0)
-            init_predict_state = self.incremental_predict(init_filter_state)
+            init_predict_state = self.incremental_predict(init_filter_state, *theta)
             xs = XType(
                 t=jnp.concatenate([jnp.asarray(t0)[None], xs.t], axis=0),
                 filtered_mean=jnp.concatenate([init_filter_state.filtered_mean[None], xs.filtered_mean], axis=0),
@@ -605,3 +666,39 @@ def _smooth_op(carry: Carry, x: XType) -> Tuple[Carry, SmoothingResult]:
             return smooth_results, smoothed_prior
 
         return smooth_results
+
+    def posterior_marginals(self, observations: jax.Array, *theta, mask: Optional[jax.Array] = None,
+                            t0: Union[jax.Array, float] = 0.) -> Union[
+        SmoothingResult, Tuple[SmoothingResult, InitialPrior]]:
+        """
+        Compute the posterior marginal distributions of the latents, p(z[t] | x[:T], theta).
+
+        Args:
+            observations: [num_time, observation_size] array of observations
+            *theta: the parameters
+            mask: [num_time] array of masks, True for missing observations
+            t0: the time of initial state
+
+        Returns:
+            A `SmoothingResult` instance, with [num_time] batched arrays.
+        """
+        filter_result = self.forward_filter(observations, *theta, mask=mask, t0=t0)
+        return self.backward_smooth(filter_result, *theta, include_prior=False)
+
+
+def convert_filter_state_to_incremental_state(filter_result: FilterResult) -> IncrementalFilterState:
+    """
+    Create an incremental filter state from a filter result.
+
+    Args:
+        filter_result: the filter result
+
+    Returns:
+        the incremental filter state
+    """
+    return IncrementalFilterState(
+        t=filter_result.t[-1],
+        log_cumulative_marginal_likelihood=filter_result.log_cumulative_marginal_likelihood[-1],
+        filtered_mean=filter_result.filtered_mean[-1],
+        filtered_cov=filter_result.filtered_cov[-1]
+    )
diff --git a/src/essm_jax/jvp_op.py b/src/essm_jax/jvp_op.py
new file mode 100644
index 0000000..cb9a08d
--- /dev/null
+++ b/src/essm_jax/jvp_op.py
@@ -0,0 +1,257 @@
+import dataclasses
+import inspect
+import os
+import warnings
+from typing import Callable, Any
+
+import jax
+import jax.numpy as jnp
+import numpy as np
+
+
+def get_grandparent_info(relative_depth: int = 7):
+    """
+    Get the file, line number and function name of the caller of the caller of this function.
+
+    Args:
+        relative_depth: the number of frames to go back from the caller of this function. Default is 6. Should be
+        enough to get out of a jax.tree.map call.
+
+    Returns:
+        str: a string with the file, line number and function name of the caller of the caller of this function.
+    """
+    # Get the grandparent frame (caller of the caller)
+    s = []
+    for depth in range(1, min(1 + relative_depth, len(inspect.stack()) - 1) + 1):
+        caller_frame = inspect.stack()[depth]
+        caller_file = caller_frame.filename
+        caller_line = caller_frame.lineno
+        caller_func = caller_frame.function
+        s.append(f"{os.path.basename(caller_file)}:{caller_line} in {caller_func}")
+    s = s[::-1]
+    s = f"at {' -> '.join(s)}"
+    return s
+
+
+def isinstance_namedtuple(obj) -> bool:
+    """
+    Check if object is a namedtuple.
+
+    Args:
+        obj: object
+
+    Returns:
+        bool
+    """
+    return (
+            isinstance(obj, tuple) and
+            hasattr(obj, '_asdict') and
+            hasattr(obj, '_fields')
+    )
+
+
+def make_linear(f: Callable, *primals0):
+    """
+    Make a linear function that approximates f around primals0.
+
+    Args:
+        f: the function to linearize
+        *primals0: the point around which to linearize
+
+    Returns:
+        the linearized function
+    """
+    f0, f_jvp = jax.linearize(f, *primals0)
+
+    def f_linear(*primals):
+        diff_primals = jax.tree.map(lambda x, x0: x - x0, primals, primals0)
+        df = f_jvp(*diff_primals)
+        return jax.tree.map(lambda y0, dy: y0 + dy, f0, df)
+
+    return f_linear
+
+
+@dataclasses.dataclass(eq=False)
+class JVPLinearOp:
+    """
+    Represents J_ij = d/d x_j f_i(x), where x is the primal value.
+
+    This is a linear operator that represents the Jacobian of a function.
+    """
+    fn: Callable  # A function R^n -> R^m
+    primals: Any | None = None  # The primal value, i.e. where jacobian is evaluated
+    more_outputs_than_inputs: bool = False  # If True, the operator is tall, i.e. m > n
+    adjoint: bool = False  # If True, the operator is transposed
+    promote_dtypes: bool = True  # If True, promote dtypes to match primal during JVP, and cotangent to match primal_out during VJP
+    linearize: bool = True  # If True, use linearized function for JVP
+
+    def __post_init__(self):
+        if not callable(self.fn):
+            raise ValueError('`fn` must be a callable.')
+
+        if self.primals is not None:
+            if isinstance_namedtuple(self.primals) or (not isinstance(self.primals, tuple)):
+                self.primals = (self.primals,)
+            if self.linearize:
+                self.linear_fn = make_linear(self.fn, *self.primals)
+
+    def __call__(self, *primals: Any) -> 'JVPLinearOp':
+        return JVPLinearOp(
+            fn=self.fn,
+            primals=primals,
+            more_outputs_than_inputs=self.more_outputs_than_inputs,
+            adjoint=self.adjoint,
+            promote_dtypes=self.promote_dtypes,
+            linearize=self.linearize
+        )
+
+    def __neg__(self):
+        return JVPLinearOp(
+            fn=lambda *args, **kwargs: jax.lax.neg(self.fn(*args, **kwargs)),
+            primals=self.primals,
+            more_outputs_than_inputs=self.more_outputs_than_inputs,
+            adjoint=self.adjoint,
+            promote_dtypes=self.promote_dtypes,
+            linearize=self.linearize
+        )
+
+    def __matmul__(self, other):
+        if not isinstance(other, (jax.Array, np.ndarray)):
+            raise ValueError(
+                'Dunder methods currently only defined for operation on arrays. '
+                'Use .matmul(...) for general tangents.'
+            )
+        if len(np.shape(other)) == 1:
+            return self.matvec(other, adjoint=self.adjoint)
+        return self.matmul(other, adjoint=self.adjoint, left_multiply=True)
+
+    def __rmatmul__(self, other):
+        if not isinstance(other, (jax.Array, np.ndarray)):
+            raise ValueError(
+                'Dunder methods currently only defined for operation on arrays. '
+                'Use .matmul(..., left_multiply=False) for general tangents.'
+            )
+        if len(np.shape(other)) == 1:
+            return self.matvec(other, adjoint=not self.adjoint)
+        return self.matmul(other, adjoint=not self.adjoint, left_multiply=False)
+
+    @property
+    def T(self) -> 'JVPLinearOp':
+        return JVPLinearOp(
+            fn=self.fn,
+            primals=self.primals,
+            more_outputs_than_inputs=self.more_outputs_than_inputs,
+            adjoint=not self.adjoint,
+            promote_dtypes=self.promote_dtypes,
+            linearize=self.linearize
+        )
+
+    def matmul(self, *tangents: Any, adjoint: bool = False, left_multiply: bool = True):
+        """
+        Implements matrix multiplication from matvec using vmap.
+
+        Args:
+            tangents: pytree of the same structure as the primals, but with appropriate more columns for adjoint=False,
+                or more rows for adjoint=True.
+            adjoint: if True, compute J.T @ v, else compute J @ v
+            left_multiply: if True, compute M @ J, else compute J @ M
+
+        Returns:
+            pytree of matching either f-space (output) or x-space (primals)
+        """
+        if left_multiply:
+            # J.T @ M or J @ M
+            in_axes = -1
+            out_axes = -1
+        else:
+            # M @ J.T or M @ J
+            in_axes = 0
+            out_axes = 0
+        if adjoint:
+            return jax.vmap(lambda *_tangent: self.matvec(*_tangent, adjoint=adjoint),
+                            in_axes=in_axes, out_axes=out_axes)(*tangents)
+        return jax.vmap(lambda *_tangent: self.matvec(*_tangent, adjoint=adjoint),
+                        in_axes=in_axes, out_axes=out_axes)(*tangents)
+
+    def matvec(self, *tangents: Any, adjoint: bool = False):
+        """
+        Compute J @ v = sum_j(J_ij * v_j) using a JVP, if adjoint is False.
+        Compute J.T @ v = sum_i(v_i * J_ij) using a VJP, if adjoint is True.
+
+        Args:
+            tangents: if adjoint=False, then  pytree of the same structure as the primals, else pytree of the same
+                structure as the output.
+            adjoint: if True, compute J.T @ v, else compute J @ v
+
+        Returns:
+            pytree of matching either f-space (output) if adjoint=False, else x-space (primals)
+        """
+        if self.primals is None:
+            raise ValueError("The primal value must be set to compute the Jacobian.")
+
+        if adjoint:
+            co_tangents = tangents
+
+            def _get_results_type(primal_out: jax.Array):
+                return primal_out.dtype
+
+            def _adjoint_promote_dtypes(co_tangent: jax.Array, dtype: jnp.dtype):
+                if co_tangent.dtype != dtype:
+                    warnings.warn(
+                        f"Promoting co-tangent dtype from {co_tangent.dtype} to {dtype}, {get_grandparent_info()}."
+                    )
+                return co_tangent.astype(dtype)
+
+            # v @ J
+            if self.linearize:
+                f_vjp = jax.linear_transpose(self.linear_fn, *self.primals)
+                primals_out = jax.eval_shape(self.linear_fn, *self.primals)
+            else:
+                primals_out, f_vjp = jax.vjp(self.fn, *self.primals)
+
+            if isinstance_namedtuple(primals_out) or (not isinstance(primals_out, tuple)):
+                # JAX squeezed structure to a single element, as the function only returns one output
+                co_tangents = co_tangents[0]
+
+            if self.promote_dtypes:
+                result_type = jax.tree.map(_get_results_type, primals_out)
+                co_tangents = jax.tree.map(_adjoint_promote_dtypes, co_tangents, result_type)
+
+            del primals_out
+            output = f_vjp(co_tangents)
+            if len(output) == 1:
+                return output[0]
+            return output
+
+        def _promote_dtype(primal: jax.Array, dtype: jnp.dtype):
+            if primal.dtype != dtype:
+                warnings.warn(f"Promoting primal dtype from {primal.dtype} to {dtype}, at {get_grandparent_info()}.")
+            return primal.astype(dtype)
+
+        def _get_result_type(primal: jax.Array):
+            return primal.dtype
+
+        primals = self.primals
+        if self.promote_dtypes:
+            result_types = jax.tree.map(_get_result_type, primals)
+            tangents = jax.tree.map(_promote_dtype, tangents, result_types)
+        # We use linearised function, so that repeated applications are cheaper.
+        if self.linearize:
+            primal_out, tangent_out = jax.jvp(self.linear_fn, primals, tangents)
+        else:
+            primal_out, tangent_out = jax.jvp(self.fn, primals, tangents)
+        return tangent_out
+
+    def to_dense(self) -> jax.Array:
+        """
+        Compute the dense Jacobian at a point.
+
+        Returns:
+            [m, n] array
+        """
+        if self.primals is None:
+            raise ValueError("The primal value must be set to compute the Jacobian.")
+
+        if self.more_outputs_than_inputs:
+            return jax.jacfwd(self.fn)(*self.primals)
+        return jax.jacrev(self.fn)(*self.primals)
diff --git a/essm_jax/pytee_utils.py b/src/essm_jax/pytee_utils.py
similarity index 92%
rename from essm_jax/pytee_utils.py
rename to src/essm_jax/pytee_utils.py
index 71626d9..e370d49 100644
--- a/essm_jax/pytee_utils.py
+++ b/src/essm_jax/pytee_utils.py
@@ -13,7 +13,7 @@ def pytree_unravel(example_tree: PT) -> Tuple[Callable[[PT], jax.Array], Callabl
     Returns functions to ravel and unravel a pytree.
 
     Args:
-        example_tree: a pytree to be unravelled
+        example_tree: a pytree to be unravelled, can also be a pytree of ShapeDtypeStruct objects instead of arrays.
 
     Returns:
         ravel_fun: a function to ravel the pytree
diff --git a/essm_jax/tests/__init__.py b/src/essm_jax/tests/__init__.py
similarity index 100%
rename from essm_jax/tests/__init__.py
rename to src/essm_jax/tests/__init__.py
diff --git a/essm_jax/tests/test_essm.py b/src/essm_jax/tests/test_essm.py
similarity index 83%
rename from essm_jax/tests/test_essm.py
rename to src/essm_jax/tests/test_essm.py
index e9ae0b8..2137ec6 100644
--- a/essm_jax/tests/test_essm.py
+++ b/src/essm_jax/tests/test_essm.py
@@ -1,16 +1,14 @@
 import time
 
 import jax
-import pytest
 
 jax.config.update('jax_enable_x64', True)
-from essm_jax.sparse import create_sparse_rep, matvec_sparse
 
 import numpy as np
 import tensorflow_probability.substrates.jax as tfp
 from jax import numpy as jnp
 
-from essm_jax.essm import ExtendedStateSpaceModel, _efficient_add_scalar_diag
+from essm_jax.essm import ExtendedStateSpaceModel, _efficient_add_diag
 
 tfpd = tfp.distributions
 
@@ -18,12 +16,12 @@
 def test_extended_state_space_model():
     num_time = 10
 
-    def transition_fn(z, t, t_next):
+    def transition_fn(z, t, t_next, *args):
         mean = 2 * z
         cov = jnp.eye(np.size(z))
         return tfpd.MultivariateNormalTriL(mean, jnp.linalg.cholesky(cov))
 
-    def observation_fn(z, t):
+    def observation_fn(z, t, *args):
         mean = z
         cov = jnp.eye(np.size(z))
         return tfpd.MultivariateNormalTriL(mean, jnp.linalg.cholesky(cov))
@@ -137,12 +135,12 @@ def _compare(key):
 
 
 def test_jvp_essm():
-    def transition_fn(z, t, t_next):
+    def transition_fn(z, t, t_next, *args):
         mean = jnp.sin(2 * z)
         cov = jnp.eye(np.size(z))
         return tfpd.MultivariateNormalTriL(mean, jnp.linalg.cholesky(cov))
 
-    def observation_fn(z, t):
+    def observation_fn(z, t, *args):
         mean = jnp.exp(z)
         cov = jnp.eye(np.size(z))
         return tfpd.MultivariateNormalTriL(mean, jnp.linalg.cholesky(cov))
@@ -193,12 +191,12 @@ def observation_fn(z, t):
 
 
 def test_speed_test_jvp_essm():
-    def transition_fn(z, t, t_next):
+    def transition_fn(z, t, t_next, *args):
         mean = jnp.sin(2 * z + t)
         cov = jnp.eye(np.size(z))
         return tfpd.MultivariateNormalTriL(mean, jnp.linalg.cholesky(cov))
 
-    def observation_fn(z, t):
+    def observation_fn(z, t, *args):
         mean = jnp.exp(z) - t
         cov = jnp.eye(np.size(z))
         return tfpd.MultivariateNormalTriL(mean, jnp.linalg.cholesky(cov))
@@ -245,12 +243,12 @@ def observation_fn(z, t):
 
 
 def test_essm_forward_simulation():
-    def transition_fn(z, t, t_next):
+    def transition_fn(z, t, t_next, *args):
         mean = z + jnp.sin(2 * jnp.pi * t / 10 * z)
         cov = 0.1 * jnp.eye(np.size(z))
         return tfpd.MultivariateNormalTriL(mean, jnp.linalg.cholesky(cov))
 
-    def observation_fn(z, t):
+    def observation_fn(z, t, *args):
         mean = z
         cov = 0.01 * jnp.eye(np.size(z))
         return tfpd.MultivariateNormalTriL(mean, jnp.linalg.cholesky(cov))
@@ -315,21 +313,21 @@ def observation_fn(z, t):
 def test__efficienct_add_scalar_diag():
     A = jnp.array([[1., 2.], [3., 4.]])
     c = 1.
-    assert jnp.all(_efficient_add_scalar_diag(A, c) == jnp.array([[2., 2.], [3., 5.]]))
+    assert jnp.all(_efficient_add_diag(A, c) == jnp.array([[2., 2.], [3., 5.]]))
 
     # bigger
     A = jnp.eye(100)
     c = 1.
-    assert jnp.all(_efficient_add_scalar_diag(A, c) == A + c * jnp.eye(100))
+    assert jnp.all(_efficient_add_diag(A, c) == A + c * jnp.eye(100))
 
 
 def test_incremental_filtering():
-    def transition_fn(z, t, t_next):
+    def transition_fn(z, t, t_next, *args):
         mean = z + z * jnp.sin(2 * jnp.pi * t / 10)
         cov = 0.1 * jnp.eye(np.size(z))
         return tfpd.MultivariateNormalTriL(mean, jnp.linalg.cholesky(cov))
 
-    def observation_fn(z, t):
+    def observation_fn(z, t, *args):
         mean = z
         cov = t * 0.01 * jnp.eye(np.size(z))
         return tfpd.MultivariateNormalTriL(mean, jnp.linalg.cholesky(cov))
@@ -359,68 +357,3 @@ def observation_fn(z, t):
                                    filter_result.log_cumulative_marginal_likelihood[i], atol=1e-5)
         np.testing.assert_allclose(filter_state.filtered_mean, filter_result.filtered_mean[i], atol=1e-5)
         np.testing.assert_allclose(filter_state.filtered_cov, filter_result.filtered_cov[i], atol=1e-5)
-
-
-@pytest.mark.parametrize('use_sparse', [False, True])
-def test_performance_sparse(use_sparse: bool):
-    # Show that using sparse rep speeds up linear system
-    n = 128
-    k = 10
-    m = np.zeros((n, n))
-    rows = np.random.randint(n, size=k)
-    cols = np.random.randint(n, size=k)
-    m[rows, cols] += 1.
-
-    if use_sparse:
-        m = create_sparse_rep(m)
-    else:
-        m = jnp.asarray(m)
-
-    def transition_fn(z, t, t_next):
-        if use_sparse:
-            mean = matvec_sparse(m, z)
-        else:
-            mean = m @ z
-        scale = jnp.ones(np.size(z))
-        return tfpd.MultivariateNormalDiag(mean, scale)
-
-    def observation_fn(z, t):
-        mean = z
-        scale = jnp.ones(np.size(z))
-        return tfpd.MultivariateNormalDiag(mean, scale)
-
-    initial_state_prior = tfpd.MultivariateNormalTriL(jnp.zeros(n), jnp.eye(n))
-
-    essm = ExtendedStateSpaceModel(
-        transition_fn=transition_fn,
-        observation_fn=observation_fn,
-        initial_state_prior=initial_state_prior,
-        materialise_jacobians=True
-    )
-
-    essm_jvp = ExtendedStateSpaceModel(
-        transition_fn=transition_fn,
-        observation_fn=observation_fn,
-        initial_state_prior=initial_state_prior,
-        materialise_jacobians=False
-    )
-
-    sample = essm.sample(jax.random.PRNGKey(0), 512)
-    filter_fn = jax.jit(
-        lambda: essm.forward_filter(sample.observation, marginal_likelihood_only=True)).lower().compile()
-    filter_jvp_fn = jax.jit(
-        lambda: essm_jvp.forward_filter(sample.observation, marginal_likelihood_only=True)).lower().compile()
-
-    t0 = time.time()
-    filter_results = filter_fn()
-    filter_results.block_until_ready()
-    t1 = time.time()
-    dt1 = t1 - t0
-    print(f"Time for essm(use_sparse={use_sparse}): {t1 - t0}")
-
-    t0 = time.time()
-    filter_results_jvp = filter_jvp_fn()
-    filter_results_jvp.block_until_ready()
-    t1 = time.time()
-    dt2 = t1 - t0
-    print(f"Time for essm_jvp(use_sparse={use_sparse}): {t1 - t0}")
diff --git a/essm_jax/tests/test_jvp_op.py b/src/essm_jax/tests/test_jvp_op.py
similarity index 52%
rename from essm_jax/tests/test_jvp_op.py
rename to src/essm_jax/tests/test_jvp_op.py
index 1799632..305a49c 100644
--- a/essm_jax/tests/test_jvp_op.py
+++ b/src/essm_jax/tests/test_jvp_op.py
@@ -1,14 +1,15 @@
+from typing import NamedTuple
+
 import jax
 import jax.numpy as jnp
-
-jax.config.update('jax_enable_x64', True)
 import numpy as np
 import pytest
 
 from essm_jax.jvp_op import JVPLinearOp
 
 
-def test_jvp_linear_op():
+@pytest.mark.parametrize('linearize', [True, False])
+def test_jvp_linear_op(linearize: bool):
     n = 4
     k = 10
     m = 2
@@ -16,9 +17,9 @@ def test_jvp_linear_op():
     def fn(x):
         return jnp.asarray([jnp.sum(jnp.sin(x) ** i) for i in range(m)])
 
-    x = jnp.arange(n).astype(float)
+    x = jnp.arange(n).astype(jnp.float32)
 
-    jvp_op = JVPLinearOp(fn)
+    jvp_op = JVPLinearOp(fn, linearize=linearize)
     jvp_op = jvp_op(x)
 
     x_space = jnp.ones((n, k))
@@ -58,14 +59,15 @@ def fn(x):
     def fn(x):
         return jnp.sum(jnp.sin(x))
 
-    jvp_op = JVPLinearOp(fn, primals=x)
+    jvp_op = JVPLinearOp(fn, primals=x, linearize=linearize)
     assert jvp_op.matvec(x_space[:, 0]).shape == ()
     assert jnp.allclose(jvp_op.matvec(x_space[:, 0]), jvp_op.to_dense() @ x_space[:, 0])
     assert jnp.allclose(jvp_op @ x_space[:, 0], jvp_op.to_dense() @ x_space[:, 0])
 
 
 @pytest.mark.parametrize('init_primals', [True, False])
-def test_multiple_primals(init_primals: bool):
+@pytest.mark.parametrize('linearize', [True, False])
+def test_multiple_primals(init_primals: bool, linearize: bool):
     n = 5
     k = 3
 
@@ -73,12 +75,12 @@ def test_multiple_primals(init_primals: bool):
     def fn(x, y):
         return jnp.stack([x * y, y, -y], axis=-1)  # [n, 3]
 
-    x = jnp.arange(n).astype(float)
-    y = jnp.arange(n).astype(float)
+    x = jnp.arange(n).astype(jnp.float32)
+    y = jnp.arange(n).astype(jnp.float32)
     if init_primals:
-        jvp_op = JVPLinearOp(fn, primals=(x, y))
+        jvp_op = JVPLinearOp(fn, primals=(x, y), linearize=linearize)
     else:
-        jvp_op = JVPLinearOp(fn)
+        jvp_op = JVPLinearOp(fn, linearize=linearize)
         jvp_op = jvp_op(x, y)
     x_space = jnp.ones((n, k))
     y_space = jnp.ones((n, k))
@@ -91,11 +93,12 @@ def fn(x, y):
         _ = (jvp_op @ (x_space, y_space)).shape == (n, 3, k)
 
 
-def test_jvp_op_dtype_promotion():
+@pytest.mark.parametrize('linearize', [True, False])
+def test_jvp_op_dtype_promotion(linearize: bool):
     def fn(x, y):
         return x + y + 0j
 
-    jvp_op = JVPLinearOp(fn, promote_dtypes=True)
+    jvp_op = JVPLinearOp(fn, promote_dtypes=True, linearize=linearize)
 
     primals = (jnp.ones(1), jnp.ones(1))
     jvp_op = jvp_op(*primals)
@@ -104,3 +107,72 @@ def fn(x, y):
 
     np.testing.assert_allclose(jvp_op.matvec(fn(*primals).astype(jnp.float32), adjoint=True),
                                jvp_op.matvec(fn(*primals), adjoint=True))
+
+
+@pytest.mark.parametrize('linearize', [True, False])
+def test_jvp_op_pytree_primals_and_cotangents(linearize: bool):
+    class Primal(NamedTuple):
+        x: jax.Array
+        y: jax.Array
+
+    class Cotangent(NamedTuple):
+        x: jax.Array
+        y: jax.Array
+        z: jax.Array
+
+    class Cotangent2(NamedTuple):
+        x: jax.Array
+        y: jax.Array
+        z: jax.Array
+        h: jax.Array
+
+    def f(x: Primal) -> tuple[Cotangent, Cotangent2]:
+        return Cotangent(x=x.x, y=x.y, z=x.x + x.y), Cotangent2(x=x.x, y=x.y, z=x.x + x.y, h=x.x - x.y)
+
+    F = JVPLinearOp(f, linearize=linearize)
+    primal = Primal(x=jnp.ones(2), y=jnp.ones(2))
+    F = F(primal)
+    cotangent = Cotangent(jnp.ones(2), jnp.ones(2), jnp.ones(2)), Cotangent2(jnp.ones(2), jnp.ones(2), jnp.ones(2),
+                                                                             jnp.ones(2))
+    tangent = Primal(jnp.ones(2), jnp.ones(2))
+
+    print(F.matvec(tangent))
+    print(F.matvec(*cotangent, adjoint=True))
+
+    def f(x: Primal, y: Primal) -> tuple[Cotangent, Cotangent2]:
+        return Cotangent(x=x.x, y=x.y, z=x.x + x.y + y.y), Cotangent2(x=x.x + y.x, y=x.y, z=x.x + x.y, h=x.x - x.y)
+
+    F = JVPLinearOp(f, linearize=linearize)
+    primal = Primal(x=jnp.ones(2), y=jnp.ones(2))
+    F = F(primal, primal)
+    cotangent = Cotangent(jnp.ones(2), jnp.ones(2), jnp.ones(2)), Cotangent2(jnp.ones(2), jnp.ones(2), jnp.ones(2),
+                                                                             jnp.ones(2))
+    tangent = Primal(jnp.ones(2), jnp.ones(2))
+
+    print(F.matvec(tangent, tangent))
+    print(F.matvec(*cotangent, adjoint=True))
+
+
+def test_linearize():
+    def f(x):
+        return jnp.sum(jnp.sin(x) ** 2) * jnp.cos(x)
+
+    x = jax.random.normal(jax.random.PRNGKey(0), (10,))
+    y = f(x)
+    g = JVPLinearOp(f, primals=x, linearize=False)
+    g_linear = JVPLinearOp(f, primals=x, linearize=True)
+
+    tangent = jax.random.normal(jax.random.PRNGKey(0), x.shape, x.dtype)
+    cotangent = jax.random.normal(jax.random.PRNGKey(0), y.shape, y.dtype)
+
+    jvp = g.matvec(tangent)
+    jvp_linear = g_linear.matvec(tangent)
+
+    print(jvp)
+    assert jnp.allclose(jvp, jvp_linear)
+
+    vjp = g.matvec(cotangent, adjoint=True)
+    vjp_linear = g_linear.matvec(cotangent, adjoint=True)
+
+    print(vjp)
+    assert jnp.allclose(vjp, vjp_linear)
diff --git a/src/essm_jax/tests/test_pytree_utils.py b/src/essm_jax/tests/test_pytree_utils.py
new file mode 100644
index 0000000..e5ff871
--- /dev/null
+++ b/src/essm_jax/tests/test_pytree_utils.py
@@ -0,0 +1,47 @@
+from typing import NamedTuple
+
+import jax
+import numpy as np
+from jax import numpy as jnp
+
+from essm_jax.pytee_utils import pytree_unravel
+
+
+def test_pytree_unravel():
+    # Simple test
+    example_tree = {'a': np.array([1, 2, 3]), 'b': np.array([[4, 5], [6, 7]])}
+    ravel_fun, unravel_fun = pytree_unravel(example_tree)
+    flat = ravel_fun(example_tree)
+    assert jnp.allclose(flat, jnp.array([1, 2, 3, 4, 5, 6, 7]))
+    assert isinstance(unravel_fun(flat), dict)
+    assert jnp.allclose(unravel_fun(flat)['a'], example_tree['a'])
+    assert jnp.allclose(unravel_fun(flat)['b'], example_tree['b'])
+
+    # with named tuple
+    class State(NamedTuple):
+        a: np.ndarray
+        b: np.ndarray
+
+    example_tree = State(np.array([1, 2, 3]), np.array([[4, 5], [6, 7]]))
+    ravel_fun, unravel_fun = pytree_unravel(example_tree)
+    flat = ravel_fun(example_tree)
+    assert jnp.allclose(flat, jnp.array([1, 2, 3, 4, 5, 6, 7]))
+    assert isinstance(unravel_fun(flat), State)
+    assert jnp.allclose(unravel_fun(flat).a, example_tree.a)
+    assert jnp.allclose(unravel_fun(flat).b, example_tree.b)
+
+    # with ShapeDtype
+    example_tree_def = dict(
+        a=jax.ShapeDtypeStruct(shape=(2, 3), dtype=jnp.float32),
+        b=jax.ShapeDtypeStruct(shape=(3, 2), dtype=jnp.float32)
+    )
+    example_tree = dict(
+        a=np.ones((2, 3), dtype=jnp.float32),
+        b=np.ones((3, 2), dtype=jnp.float32)
+    )
+    ravel_fun, unravel_fun = pytree_unravel(example_tree_def)
+    flat = ravel_fun(example_tree)
+    assert jnp.allclose(flat, jnp.ones(12))
+    assert isinstance(unravel_fun(flat), dict)
+    assert jnp.allclose(unravel_fun(flat)['a'], example_tree['a'])
+    assert jnp.allclose(unravel_fun(flat)['b'], example_tree['b'])