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 2 | ```
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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