indepmetrop {LearnBayes}R Documentation

Independence Metropolis independence chain of a posterior distribution

Description

Simulates iterates of an independence Metropolis chain with a normal proposal density for an arbitrary real-valued posterior density defined by the user

Usage

indepmetrop(logpost,proposal,start,m,data)

Arguments

logpost function defining the log posterior density
proposal a list containing mu, an estimated mean and var, an estimated variance-covariance matrix, of the normal proposal density
start vector containing the starting value of the parameter
m the number of iterations of the chain
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 the acceptance rate of the algorithm

Author(s)

Jim Albert

Examples

data=c(6,2,3,10)
proposal=list(mu=array(c(2.3,-.1),c(2,1)),var=diag(c(1,1)))
start=array(c(0,0),c(1,2))
m=1000
fit=indepmetrop(logctablepost,proposal,start,m,data)

[Package LearnBayes version 2.0 Index]