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

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 …

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 …

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 …

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 …