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Activity Number: 430
Type: Contributed
Date/Time: Tuesday, August 6, 2013 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Computing
Abstract - #310328
Title: Bayesian Statistical Modeling in Python Using PyMC
Author(s): Christopher Fonnesbeck*+ and John Salvatier
Companies: Vanderbilt University and University of Washington
Keywords: MCMC ; software ; Python ; Bayesian

Markov chain Monte Carlo (MCMC) is the *de facto* standard computational approach for Bayesian modeling and estimation. PyMC implements a suite of MCMC sampling algorithms in Python and provides a simple, intuitive and extensible way to specify and fit models. Additionally, PyMC provides facilities for output summarization, plotting, goodness-of-fit and convergence diagnostics. The most recent version (version 3) includes gradient-based sampling methods and automatic parallel sampling, which profoundly improve the efficiency of fitting models using MCMC.

Authors who are presenting talks have a * after their name.

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