API — PyMC dev documentation (original) (raw)

• 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
• Testing

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 theArviZ. 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 theBambi. 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.