Description Usage Arguments Examples
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
1 | gibbsNormal(N, initial, priorparam, n, xbar, s)
|
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 |
1 | 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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