Learn PyMC & Bayesian modeling # Installation Notebooks on core features Books Videos and Podcasts Consulting Glossary

Installation # We recommend using Anaconda (or Miniforge) to install Python on your local machine, which allows for packages to be installed using its conda utility. Once you have installed one of the …

Learn PyMC & Bayesian modeling # Installation Notebooks on core features Books Videos and Podcasts Consulting Glossary

PyMC is an open source probabilistic programming framework written in Python that uses PyTensor to compute gradients via automatic differentiation, as well as compiling probabilistic programs on-the-fly …

trace pymc.backends.base.MultiTrace | pymc.backends.zarr.ZarrTrace | arviz.InferenceData A MultiTrace, InferenceData or ZarrTrace object that contains the samples.

kernel SMC Kernel, optional SMC kernel used. Defaults to pymc.smc.smc.IMH (Independent Metropolis Hastings) start dict or array of dict, optional Starting point in parameter space. It should be a list of …

pymc.gp.util pymc.logprob.basic pymc.logprob.transforms pymc.math pymc.model.core pymc.model.fgraph pymc.model.transform.conditioning pymc.model.transform.optimization …

The tensors to which mini-bathced samples are supplied are handled separately by using callbacks in Inference.fit() method that change storage of shared PyTensor variable or by pymc.generator() that …

PyMC is a Python package for Bayesian statistical modeling built on top of PyTensor. This document aims to explain the design and implementation of probabilistic programming in PyMC, with …

PyMC balances this through the use of transforms. A transform instance can be passed to the constructor of a random variable to tell the sampler how to move between the underlying …