API

• Distributions
• Gaussian Processes
• Model
• Samplers
• Variational Inference
• Sequential Monte Carlo
• Data
• Ordinary differential equations (ODEs)
• Probability
• Tuning
• Math
• PyTensor utils
• shape_utils
• Storage backends
• Other utils

Dimensionality

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

API extensions

Plots, stats and diagnostics

Plots, stats and diagnostics are delegated to the ArviZ. 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 ArviZ documentation.

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

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.