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Pystan: Bayesian Inference for Fun and Profit
--client
depy
--show
depy_2016
--room room_LL104 10978 --force
Next: 11 Mapping Collaborations and Knowledge in Scientific Research and Wikipedia: A How-to Approach
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Marks
Author(s):
Stephen Hoover
Location
Room LL104
Date
may Sat 07
Days Raw Files
Start
13:30
First Raw Start
13:25
Duration
30:00
Offset
0:04:36
End
14:00
Last Raw End
14:05
Chapters
00:00
0:02:41
0:12:41
0:22:41
0:27:46
Total cuts_time
30 min.
http://mdp.cdm.depaul.edu/DePy2016/default/schedule
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Probabilistic programming languages offer a flexible and expressive way to model data by treating random variables as first-class objects. Stan is a popular and well-supported library which allows users to write models in the Stan programming language and use MCMC methods to perform Bayesian inference. Stan itself is written in C++, and has a Python interface through the PyStan package. In this talk, I'll show off some of the capabilities of PyStan and go through a simple practical example of Bayesian inference in Python.
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production notes
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