Naive Gibbs Sampling with Metropolis Steps

Longhai Li, Department of Mathematics and Statistics, University of Saskatchewan

Copyright

Permission is granted for anyone to copy, use, modify, or distribute these programs and accompanying documents for any purpose, provided this copyright notice is retained and prominently displayed, and note is made of any changes made to these programs. These programs and documents are distributed without any warranty, express or implied. As the programs were written for research purposes only, they have not been tested to the degree that would be advisable in any important application. All use of these programs is entirely at the user's own risk.

Description

This package provides two generic functions for performing Markov chain sampling in a naive way for a user-defined target distribution, which involves only continuous variables. The function "gibbs_met" performs Gibbs sampling with each 1-dimensional distribution sampled with Metropolis update using Gaussian proposal distribution centered at the previous state. The function "met_gaussian" updates the whole state with Metropolis method using independent Gaussian proposal distribution centered at the previous state. The sampling is carried out without considering any special tricks for improving efficiency. This package is aimed at only routine applications of MCMC in moderate-dimensional problems.

Source Packages and Documentations

gibbs.met_1.1-3, first released 02/04/2008, Documentation: gibbs.met_1.1-3.pdf, source package: gibbs.met_1.1-3.tar.gz
gibbs.met_1.1-2, first released 26/03/2008, Documentation: gibbs.met_1.1-2.pdf, source package: gibbs.met_1.1-2.tar.gz

References

The methods can be found from any standard textbook introducing Markov chain Monte Carlo, for example this review available online.

Instruction of Installing an R package and Using R

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