gibbs: Metropolis within Gibbs sampling algorithm of a posterior...

In LearnBayes: Functions for Learning Bayesian Inference

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

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)

Example output

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