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Minor doc clean up #428

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Dec 22, 2022
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Minor doc clean up #428

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4 changes: 2 additions & 2 deletions blackjax/kernels.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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
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1 change: 1 addition & 0 deletions docs/howto_use_ppl.rst
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Expand Up @@ -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
2 changes: 1 addition & 1 deletion examples/SGMCMC.md
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Expand Up @@ -11,7 +11,7 @@ kernelspec:
name: python3
file_format: mystnb
mystnb:
execution_timeout: 200
execution_timeout: 300
merge_streams: true
---

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2 changes: 1 addition & 1 deletion examples/howto_use_numpyro.md
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Expand Up @@ -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")
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2 changes: 1 addition & 1 deletion examples/howto_use_tfp.md
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Expand Up @@ -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")
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