Gibbs sampler for a normal random sample with a semi-conjugate prior

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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

1
  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

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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)

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