Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
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Authors: Anand Patil, David Huard, Christopher J. Fonnesbeck
Title: PyMC: Bayesian Stochastic Modelling in Python
Abstract: This user guide describes a Python package, PyMC, that allows users to efficiently code a probabilistic model and draw samples from its posterior distribution using Markov chain Monte Carlo techniques.

Page views:: 12223. Submitted: 2008-12-22. Published: 2010-07-16.
Paper: PyMC: Bayesian Stochastic Modelling in Python     Download PDF (Downloads: 13662)
pymc-2.1beta.tar.gz: Python source package Download (Downloads: 1066; 1MB) Python example code from the paper Download (Downloads: 1034; 6KB)

DOI: 10.18637/jss.v035.i04

This work is licensed under the licenses
Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.