Author(s):	Christopher Fonnesbeck
Location	Poster III
Date	mar Sun 11	Days Raw Files
Start	08:45	First Raw Start	14:45
Duration	00:05:00	Offset	6:00:10
End	08:50	Last Raw End	14:45
Chapters	00:00 0:00:06
Total cuts_time	0 min.

http://us.pycon.org/2012/schedule/presentation/501/
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assets release.pdf 35_PyMC_Markov_chain_Monte_Carlo_in_Python.json
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Description:	PyMC implements a suite of Markov chain Monte Carlo (MCMC) sampling algorithms making it extremely flexible, extensible and applicable to a large suite of statistical modeling problems. PyMC's clear, concise syntax allows scientists to flexibly implement Bayesian models without hand-coding samplers. [video is just a shot of the poser. go over achiever AV team.] markdown
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