rwmetrop: Random walk Metropolis algorithm of a posterior distribution

In LearnBayes: Functions for Learning Bayesian Inference

Description

Simulates iterates of a random walk Metropolis chain for an arbitrary real-valued posterior density defined by the user

Usage

rwmetrop(logpost,proposal,start,m,...)

Arguments

logpost	function defining the log posterior density
proposal	a list containing var, an estimated variance-covariance matrix, and scale, the Metropolis scale factor
start	vector containing the starting value of the parameter
m	the number of iterations of the chain
...	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)
varcov=diag(c(1,1))
proposal=list(var=varcov,scale=2)
start=array(c(1,1),c(1,2))
m=1000
s=rwmetrop(logctablepost,proposal,start,m,data)

Example output

LearnBayes documentation built on May 1, 2019, 7:03 p.m.