# normgibbs: A simple Gibbs sampler for Bayesian inference for the mean... In smfsb: Stochastic Modelling for Systems Biology

## Description

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

## Usage

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

## Arguments

 `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. `mean(x)` for a vector `x`. `ssquared` The sample variance of the data. eg. `var(x)` for a vector `x`.

## Value

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

`rcfmc`, `metrop`, `mcmcSummary`

## Examples

 ``` 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) ```

### Example output ```Loading required package: abind