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 …

Here, we present a primer on the use of PyMC for solving general Bayesian statistical inference and prediction problems. We will first see the basics of how to use PyMC, motivated by a …

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

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

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 …

In this notebook we want to give an introduction of how PyMC models translate to Aesara graphs. The purpose is not to give a detailed description of all aesara ’s capabilities but rather focus on …

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 …

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 …