mwgGamma: Metropolis within Gibbs algorithm for a gamma random sample

In mas3321: Bayes normal linear model and basic MCMC

Description

Simulates realisations from the posterior distribution for the index and shape parameters in a gamma distribution based on a random sample and independent gamma priors by using a Metropolis within Gibbs algorithm and a normal random walk proposal for the index parameter

Usage

  mwgGamma(N, initial, innov, priorparam, n, xbar, xgbar,
    show = TRUE)

Arguments

N	length of MCMC chain
initial	starting value for the algorithm
innov	standard deviation of normal random walk innovation for index parameter
priorparam	prior parameters a,b,c,d
n	size of random sample
xbar	(arithmetic) mean of random sample
xgbar	geometric mean of random sample
show	logical. If true then acceptance rate for the proposals will be given

Examples

mcmcAnalysis(mwgGamma(100,(0.62/0.4)^2,0.8,c(2,1,3,1),50,0.62,0.46),rows=2)

mas3321 documentation built on May 2, 2019, 4:42 p.m.