Description Usage Arguments Value See Also Examples

This function runs a simple Gibbs sampler for the Bayesian posterior distribution of the mean and precision given a normal random sample.

1 | ```
normgibbs(N, n, a, b, cc, d, xbar, ssquared)
``` |

`N` |
The number of iterations of the Gibbs sampler. |

`n` |
The sample size of the normal random sample. |

`a` |
The shape parameter of the gamma prior on the sample precision. |

`b` |
The scale parameter of the gamma prior on the sample precision. |

`cc` |
The mean of the normal prior on the sample mean. |

`d` |
The precision of the normal prior on the sample mean. |

`xbar` |
The sample mean of the data. eg. |

`ssquared` |
The sample variance of the data. eg. |

An R matrix object containing the samples of the Gibbs sampler.

1 2 3 4 5 6 7 8 9 10 11 12 13 | ```
postmat=normgibbs(N=1100,n=15,a=3,b=11,cc=10,d=1/100,xbar=25,ssquared=20)
postmat=postmat[101:1100,]
op=par(mfrow=c(3,3))
plot(postmat)
plot(postmat,type="l")
plot.new()
plot(ts(postmat[,1]))
plot(ts(postmat[,2]))
plot(ts(sqrt(1/postmat[,2])))
hist(postmat[,1],30)
hist(postmat[,2],30)
hist(sqrt(1/postmat[,2]),30)
par(op)
``` |

```
Loading required package: abind
Loading required package: parallel
```

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