mwgGamma: Metropolis within Gibbs algorithm for a gamma random sample

In nclbayes: Functions, Demos and Examples of Bayesian Analysis 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	a 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)

nclbayes documentation built on May 2, 2019, 5:53 p.m.