PyMC3 -> PyMC (#769)

pymc-devs · Feb 5, 2025 · 18cb11d · 18cb11d
1 parent c73b785
commit 18cb11d
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diff --git 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 <pymc.model.core.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 <pymc.model.core.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
@@ -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 <pymc.model.core.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 <pymc.model.core.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