gibbs {LearnBayes}R Documentation

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,data)

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 data that is used in the function logpost

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

Author(s)

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)

[Package LearnBayes version 2.0 Index]