Minor doc clean up (#428)

blackjax-devs · Dec 22, 2022 · dbb1dc9 · dbb1dc9
1 parent cc5d3f5
commit dbb1dc9
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Showing 5 changed files with 6 additions and 5 deletions.
diff --git a/blackjax/kernels.py b/blackjax/kernels.py
@@ -418,13 +418,13 @@ class mgrad_gaussian:
     .. code::
 
         mgrad_gaussian = blackjax.mgrad_gaussian(f, C, use_inverse=False, mean=m)
-        state = latent_gaussian.init(zeros)  # Starting at the mean of the prior
+        state = mgrad_gaussian.init(zeros)  # Starting at the mean of the prior
         new_state, info = mgrad_gaussian.step(rng_key, state, delta)
 
     We can JIT-compile the step function for better performance
 
     .. code::
-        step = jax.jit(latent_gaussian.step)
+        step = jax.jit(mgrad_gaussian.step)
         new_state, info = step(rng_key, state, delta)
 
     Parameters

diff --git a/docs/howto_use_ppl.rst b/docs/howto_use_ppl.rst
@@ -7,4 +7,5 @@ Use the model I built with X?
    examples/howto_use_aesara.md
    examples/howto_use_numpyro.md
    examples/howto_use_oryx.md
+   examples/howto_use_pymc.md
    examples/howto_use_tfp.md
diff --git a/examples/SGMCMC.md b/examples/SGMCMC.md
@@ -11,7 +11,7 @@ kernelspec:
   name: python3
 file_format: mystnb
 mystnb:
-  execution_timeout: 200
+  execution_timeout: 300
   merge_streams: true
 ---
 

diff --git a/examples/howto_use_numpyro.md b/examples/howto_use_numpyro.md
@@ -163,7 +163,7 @@ fig.set_size_inches(12, 10)
 for i in range(J):
     axes[i][0].plot(samples["theta_base"][:, i])
     axes[i][0].title.set_text(f"School {i} relative treatment effect chain")
-    sns.kdeplot(samples["theta_base"][:, i], ax=axes[i][1], shade=True)
+    sns.kdeplot(samples["theta_base"][:, i], ax=axes[i][1], fill=True)
     axes[i][1].title.set_text(f"School {i} relative treatment effect distribution")
 axes[J - 1][0].set_xlabel("Iteration")
 axes[J - 1][1].set_xlabel("School effect")

diff --git a/examples/howto_use_tfp.md b/examples/howto_use_tfp.md
@@ -176,7 +176,7 @@ fig.set_size_inches(12, 10)
 for i in range(num_schools):
     axes[i][0].plot(school_effects_samples[:, i])
     axes[i][0].title.set_text(f"School {i} treatment effect chain")
-    sns.kdeplot(school_effects_samples[:, i], ax=axes[i][1], shade=True)
+    sns.kdeplot(school_effects_samples[:, i], ax=axes[i][1], fill=True)
     axes[i][1].title.set_text(f"School {i} treatment effect distribution")
 axes[num_schools - 1][0].set_xlabel("Iteration")
 axes[num_schools - 1][1].set_xlabel("School effect")