# 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

## 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

Click here.