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 conjugate normal-gamma prior distribution by using a Gibbs sampler

1 | ```
gibbsNormal2(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(gibbsNormal2(N=100,initial=c(5.41,25),priorparam=c(5.41,0.25,2.5,0.1),n=23,xbar=5.4848,s=0.1882),rows=2)
``` |

mas3321 documentation built on May 31, 2017, 1:50 a.m.

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