gibbsNormal: Gibbs sampler for a normal random sample with a...

In mas3321: Bayes normal linear model and basic MCMC

Description

Simulates realisations from the posterior distribution for the mean and precision in a normal distribution based on a random sample and a semi-conjugate prior by using a Gibbs sampler

Usage

  gibbsNormal(N, initial, priorparam, n, xbar, s)

Arguments

N	length of MCMC chain
initial	starting value for the algorithm
priorparam	prior parameters b,c,g,h
n	size of random sample
xbar	mean of random sample
s	standard deviation of random sample

Examples

mcmcAnalysis(gibbsNormal(N=100,initial=c(10,0.25),priorparam=c(10,1/100,3,12),n=100,xbar=15,s=4.5),rows=2)

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