edit TS section; add Shumway to references

elizavetasemenova · Nov 21, 2024 · 08d1acf · 08d1acf
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diff --git a/06_Monte_Carlo.ipynb b/06_Monte_Carlo.ipynb
@@ -6,7 +6,7 @@
    "source": [
     "# The Monte Carlo method\n",
     "\n",
-    "The Monte Carlo {cite}`metropolis1949monte, robert1999monte` method is a computational technique that uses <font color='orange'>random sampling</font> to estimate complex mathematical outcomes or solve problems that might be <font color='orange'>deterministic</font> in nature.\n",
+    "The Monte Carlo {cite}`metropolis1949monte, robert_monte_2004` method is a computational technique that uses <font color='orange'>random sampling</font> to estimate complex mathematical outcomes or solve problems that might be <font color='orange'>deterministic</font> in nature.\n",
     "\n",
     "The name “Monte Carlo” for the Monte Carlo methods has an origin that ties back to the famous Monte Carlo Casino located in Monaco. This name was not chosen because of any direct association with the mathematical principles behind these methods, but rather for its metaphorical connection to randomness and chance, which are central elements in both gambling and Monte Carlo simulations.\n",
     "\n",

diff --git a/11_Bayesian_workflow.ipynb b/11_Bayesian_workflow.ipynb
@@ -711,19 +711,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "def plot_regression(x, y_mean, y_hpdi, height, ttl='Predictions with 89% CI)'):\n",
-    "    # Sort values for plotting by x axis\n",
+    "    # sort values for plotting by x axis\n",
     "    idx = jnp.argsort(x)\n",
     "    weight = x[idx]\n",
     "    mean = y_mean[idx]\n",
     "    hpdi = y_hpdi[:, idx]\n",
     "    ht = height[idx]\n",
     "\n",
-    "    # Plot\n",
+    "    # plot\n",
     "    fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(6, 6))\n",
     "    ax.plot(weight, mean, color='teal')\n",
     "    ax.plot(weight, ht, 'o', color='orangered')\n",
@@ -764,7 +764,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -790,11 +790,11 @@
     }
    ],
    "source": [
-    "# Inference\n",
+    "# inference\n",
     "rng_key = random.PRNGKey(0)\n",
     "rng_key, rng_key_ = random.split(rng_key)\n",
     "\n",
-    "# Run NUTS\n",
+    "# run NUTS\n",
     "kernel = NUTS(model)\n",
     "mcmc = MCMC(kernel, num_warmup=1000, num_samples=2000, num_chains=4, progress_bar=False)\n",
     "mcmc.run(rng_key_, weight=weight, height=height)\n",
@@ -804,7 +804,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -832,7 +832,7 @@
     }
    ],
    "source": [
-    "# Check convergence\n",
+    "# check convergence\n",
     "mcmc.print_summary()\n",
     "data = az.from_numpyro(mcmc)\n",
     "az.plot_trace(data, compact=True);"
@@ -845,7 +845,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -860,7 +860,7 @@
     }
    ],
    "source": [
-    "# Posterior predictive\n",
+    "# posterior predictive\n",
     "rng_key, rng_key_ = random.split(rng_key)\n",
     "predictive = Predictive(model, samples_1)\n",
     "posterior_predictions = predictive(rng_key_, weight=weight)\n",