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 …

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 simple …

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

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 simple …

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 PyMC, with …

Each of the terms below has a specific semantic and computational definition in PyMC. While we share them here they will make much more sense when viewed in the examples below.

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 …

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 the main …