gibbs.met: Naive Gibbs Sampling with Metropolis Steps

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.

Author
Longhai Li <longhai@math.usask.ca>
Date of publication
2012-10-29 08:58:54
Maintainer
Longhai Li <longhai@math.usask.ca>
License
GPL (>= 2)
Version
1.1-3
URLs

View on CRAN

Man pages

gibbs
Gibbs sampling with Metropolis steps and multivariate...

Files in this package

gibbs.met
gibbs.met/NAMESPACE
gibbs.met/man
gibbs.met/man/gibbs.Rd
gibbs.met/DESCRIPTION
gibbs.met/MD5
gibbs.met/R
gibbs.met/R/gibbs-met.R
gibbs.met/R/firstlib.R