Intermediate # Introductory Overview of PyMC shows PyMC code in action Example notebooks: PyMC Example Gallery GLM: Linear regression Prior and Posterior Predictive Checks Comparing models: …

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 …

Introductory Overview of PyMC # Note: This text is partly based on the PeerJ CS publication on PyMC by John Salvatier, Thomas V. Wiecki, and Christopher Fonnesbeck. Abstract # Probabilistic …

At a glance # Beginner # Book: Bayesian Methods for Hackers Book: Bayesian Analysis with Python Intermediate # Introductory Overview of PyMC shows PyMC 4.0 code in action Example notebooks: …

Explore PyMC's documentation to learn Bayesian modeling and probabilistic programming with Python, featuring tutorials, examples, and advanced topics for all skill levels.

pymc.smc.sample_smc # pymc.smc.sample_smc(draws=2000, kernel=<class 'pymc.smc.kernels.IMH'>, *, start=None, model=None, random_seed=None, chains=None, cores=None, blas_cores=None, …

All modules for which code is available pymc.backends.arviz pymc.backends.base pymc.backends.ndarray pymc.backends.zarr pymc.blocking pymc.data pymc.dims ...

PyMC Developer Guide # 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 …

pymc.math.log1pexp # pymc.math.log1pexp = Elemwise (scalar_op=Softplus,inplace_pattern=<frozendict {}>) # Compute log (1 + exp (x)), also known as …

pymc.sample # pymc.sample(draws=1000, *, tune=1000, chains=None, cores=None, random_seed=None, progressbar=True, progressbar_theme=None, quiet=False, step=None, …