LearnBayes (version 2.15.2)

gibbs: Metropolis within Gibbs sampling algorithm of a posterior distribution

Description

Implements a Metropolis-within-Gibbs sampling algorithm for an arbitrary real-valued posterior density defined by the user

Usage

gibbs(logpost,start,m,scale,...)

Value

par

a matrix of simulated values where each row corresponds to a value of the vector parameter

accept

vector of acceptance rates of the Metropolis steps of the algorithm

Arguments

logpost

function defining the log posterior density

start

array with a single row that gives the starting value of the parameter vector

m

the number of iterations of the chain

scale

vector of scale parameters for the random walk Metropolis steps

...

data that is used in the function logpost

Author

Jim Albert

Examples

data=c(6,2,3,10)
start=array(c(1,1),c(1,2))
m=1000
scale=c(2,2)
s=gibbs(logctablepost,start,m,scale,data)