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cs-677sp2010:mcmc-sample-code [2014/12/12 20:34] (current)
ryancha created
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 +We have provided some sample code to help you understand MCMC techniques such as Gibbs sampling, Metropolis, and Gibbs-Metropolis.
  
 +Note that all of the faculty examples use the data in [http://​aml.cs.byu.edu/​~kseppi/​cs677sp07files/​mcmc/​faculty.dat faculty.dat]
 +
 +== Gibbs Sampling ==
 +
 +In the following example, Gibbs sampling is used with sampling from the complete conditionals.
 +
 +[http://​aml.cs.byu.edu/​~kseppi/​cs677sp08files/​gibbs/​fac-gibbs.py fac-gibbs.py]
 +
 +== Metropolis ==
 +
 +In the following example, the Metropolis algorithm is used to sample from a Cauchy distribution.
 +
 +[http://​aml.cs.byu.edu/​~kseppi/​cs677sp08files/​metropolis/​cauchy.py cauchy.py]
 +
 +== Gibbs-Metropolis Combo ==
 +
 +This code implements a combo MCMC algorithm using Gibbs sampling, with Metropolis sampling at each node:
 +
 +[http://​aml.cs.byu.edu/​~kseppi/​cs677sp08files/​combo/​fac-combo.py fac-combo.py]
 +
 +
 +== Evilplot ==
 +
 +Evilplot files are listed below. A simple way to get everything needed for evilplot is to run the following command: ​
 +   git clone git://​aml.cs.byu.edu/​evilplot.git
 +
 +[http://​aml.cs.byu.edu/​~kseppi/​cs677sp08files/​evilplot/​__init__.py __init__.py]
 +
 +[http://​aml.cs.byu.edu/​~kseppi/​cs677sp08files/​evilplot/​param.py param.py]
 +
 +[http://​aml.cs.byu.edu/​~kseppi/​cs677sp08files/​evilplot/​plot.py plot.py]
 +
 +[http://​aml.cs.byu.edu/​~kseppi/​cs677sp08files/​evilplot/​plotitems.py plotitems.py]
 +
 +[http://​aml.cs.byu.edu/​~kseppi/​cs677sp08files/​evilplot/​util.py util.py]
cs-677sp2010/mcmc-sample-code.txt ยท Last modified: 2014/12/12 20:34 by ryancha
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