LearnBayes (version 2.15.1)

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,...)

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

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

Examples

# NOT RUN {
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)
# }