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 — cs-677sp2010:mcmc-sample-code [2014/12/12 20:34] (current)ryancha created 2014/12/12 20:34 ryancha created 2014/12/12 20:34 ryancha created Line 1: Line 1: + 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 · Last modified: 2014/12/12 20:34 by ryancha 