From 3b0b299c5b60add87c8ea59dce6e3517e2a4fe17 Mon Sep 17 00:00:00 2001 From: Will Dean Date: Wed, 29 Jan 2025 14:13:41 +0100 Subject: [PATCH] PyMC3 -> PyMC --- examples/howto/profiling.ipynb | 6 +++--- examples/howto/profiling.myst.md | 4 ++-- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/examples/howto/profiling.ipynb b/examples/howto/profiling.ipynb index be80f0988..1e09f294f 100644 --- a/examples/howto/profiling.ipynb +++ b/examples/howto/profiling.ipynb @@ -6,7 +6,7 @@ "source": [ "(profiling)=\n", "# Profiling\n", - "Sometimes computing the likelihood is not as fast as we would like. Theano provides handy profiling tools which are wrapped in PyMC3 by {func}`model.profile `. This function returns a `ProfileStats` object conveying information about the underlying Theano operations. Here we'll profile the likelihood and gradient for the stochastic volatility example.\n", + "Sometimes computing the likelihood is not as fast as we would like. Theano provides handy profiling tools which are wrapped in PyMC by {func}`model.profile `. This function returns a `ProfileStats` object conveying information about the underlying Theano operations. Here we'll profile the likelihood and gradient for the stochastic volatility example.\n", "\n", "First we build the model." ] @@ -27,7 +27,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Running on PyMC3 v5.10.0\n" + "Running on PyMC v5.10.0\n" ] } ], @@ -36,7 +36,7 @@ "import pandas as pd\n", "import pymc as pm\n", "\n", - "print(f\"Running on PyMC3 v{pm.__version__}\")" + "print(f\"Running on PyMC v{pm.__version__}\")" ] }, { diff --git a/examples/howto/profiling.myst.md b/examples/howto/profiling.myst.md index 84809083e..2b81b02a4 100644 --- a/examples/howto/profiling.myst.md +++ b/examples/howto/profiling.myst.md @@ -12,7 +12,7 @@ kernelspec: (profiling)= # Profiling -Sometimes computing the likelihood is not as fast as we would like. Theano provides handy profiling tools which are wrapped in PyMC3 by {func}`model.profile `. This function returns a `ProfileStats` object conveying information about the underlying Theano operations. Here we'll profile the likelihood and gradient for the stochastic volatility example. +Sometimes computing the likelihood is not as fast as we would like. Theano provides handy profiling tools which are wrapped in PyMC by {func}`model.profile `. This function returns a `ProfileStats` object conveying information about the underlying Theano operations. Here we'll profile the likelihood and gradient for the stochastic volatility example. First we build the model. @@ -21,7 +21,7 @@ import numpy as np import pandas as pd import pymc as pm -print(f"Running on PyMC3 v{pm.__version__}") +print(f"Running on PyMC v{pm.__version__}") ``` ```{code-cell} ipython3