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