beaver.gibbs: Gibbs Sampler for Normal Changepoint Model, Practical 11.7
In SMPracticals: Practicals for Use with Davison (2003) Statistical Models

Description Usage Arguments Details Value Author(s) References Examples

This function implements a Gibbs sampler for the normal changepoint model applied to the beaver temperature data used in Example 6.22 and Practical 11.7 of Davison (2003), which should be consulted for details.

1	beaver.gibbs(init, y, R = 10, a = 1, b = 0.05)

`init`	Initial values for parameters
`y`	A series of normal observations
`R`	Number of iterations of sampler
`a`	Value of a hyperparameter
`b`	Value of a hyperparameter

This is provided simply so that readers spend less time typing. It is not intended to be robust and general code.

A matrix of size R x 6, whose first four columns contain the values of the parameters for the iterations. Columns 5 and 6 contain the log likelihood and log prior for that iteration.

Anthony Davison (anthony.davison@epfl.ch)

Davison, A. C. (2003) Statistical Models. Cambridge University Press. Practical 11.7.

## From Example 11.7:
data(beaver)
system.time( gibbs.out <- beaver.gibbs(c(36, 40, 3, 38), beaver$temp, R=1000))
par(mfrow=c(2,3))
plot.ts(gibbs.out[,1],main="mu1") # time series plot for mu1
plot.ts(gibbs.out[,2],main="mu2") # time series plot for mu2
plot.ts(gibbs.out[,3],main="lambda") # time series plot for lambda
plot.ts(gibbs.out[,4],main="gamma") # time series plot for gamma
plot.ts(gibbs.out[,5],main="log likelihood")  # and of log likelihood