api.rst

API

.. toctree::
   :maxdepth: 1

   api/distributions
   api/gp
   api/model
   api/samplers
   api/vi
   api/smc
   api/data
   api/ode
   api/logprob
   api/tuning
   api/math
   api/pytensorf
   api/shape_utils
   api/backends
   api/misc

Dimensionality

PyMC provides numerous methods, and syntactic sugar, to easily specify the dimensionality of Random Variables in modeling. Refer to :ref:`dimensionality` notebook to see examples demonstrating the functionality.

API extensions

Plots, stats and diagnostics

Plots, stats and diagnostics are delegated to the :doc:`ArviZ <arviz:index>`. library, a general purpose library for "exploratory analysis of Bayesian models".

• Functions from the arviz.plots module are available through pymc.<function> or pymc.plots.<function>, but for their API documentation please refer to the :ref:`ArviZ documentation <arviz:plot_api>`.
• Functions from the arviz.stats module are available through pymc.<function> or pymc.stats.<function>, but for their API documentation please refer to the :ref:`ArviZ documentation <arviz:stats_api>`.

ArviZ is a dependency of PyMC and so, in addition to the locations described above, importing ArviZ and using arviz.<function> will also work without any extra installation.

Generalized Linear Models (GLMs)

Generalized Linear Models are delegated to the Bambi. library, a high-level Bayesian model-building interface built on top of PyMC.

Bambi is not a dependency of PyMC and should be installed in addition to PyMC to use it to generate PyMC models via formula syntax.