Fix small typos

blackjax-devs · Jan 14, 2023 · 25eee35 · 25eee35
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diff --git a/docs/examples/howto_use_aesara.md b/docs/examples/howto_use_aesara.md
@@ -13,7 +13,7 @@ kernelspec:
 
 # Use with Aesara models
 
-Blackjax accepts any log-probability function as long as it is compatible with `jax.jit`, `jax.grad` (for gradient-based samplers) and `jax.vmap`. In this example we will show ho we can use [Aesara](https://github.com/aesara-devs/aesara) as a modeling language and Blackjax as an inference library.
+Blackjax accepts any log-probability function as long as it is compatible with `jax.jit`, `jax.grad` (for gradient-based samplers) and `jax.vmap`. In this example we will show how we can use [Aesara](https://github.com/aesara-devs/aesara) as a modeling language and Blackjax as an inference library.
 
 ``` {admonition} Before you start
 You will need [Aesara](https://github.com/aesara-devs/aesara) and [AePPL](https://github.com/aesara-devs/aeppl) to run this example. Please follow the installation instructions on their respective repository.

diff --git a/docs/examples/howto_use_pymc.md b/docs/examples/howto_use_pymc.md
@@ -44,7 +44,7 @@ with pm.Model() as model:
 
     theta = pm.Normal("theta", mu=0, sigma=1, shape=J)
     theta_1 = mu + tau * theta
-    obs = pm.Normal("obs", mu=theta, sigma=sigma, shape=J, observed=y)
+    obs = pm.Normal("obs", mu=theta_1, sigma=sigma, shape=J, observed=y)
 ```