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Editors-in-chief: Bettina Grün, Torsten Hothorn, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
PyMC: Bayesian Stochastic Modelling in Python | Patil | Journal of Statistical Software
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.

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Paper: PyMC: Bayesian Stochastic Modelling in Python     Download PDF (Downloads: 9016)
Supplements:
pymc-2.1beta.tar.gz: Python source package Download (Downloads: 911; 1MB)
v35i04.zip: Python example code from the paper Download (Downloads: 899; 6KB)

DOI: 10.18637/jss.v035.i04

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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.