ABSTRACT
This chapter describes the parametric Bayesian analyses are performed on the breast cancer data set using Just Another Gibbs Sampler in combination with R. SAS allows to fit parametric survival models in a Bayesian way. The BAYES statement instructs the procedure to choose for a Bayesian analysis based on a MCMC algorithm, here Gibbs sampling. The chapter focuses on the Bayesian approach which is quite natural for this type of model, especially in combination with interval-censored data. Extension to interval-censored data is straightforward within the Bayesian framework and it is discussed in more detail by Arnost Komarek and implemented in the R package mixAK. Bayesian estimation of classical Gaussian mixtures using the reversible jump Markov chain Monte Carlo algorithm allowing also for censored data has been implemented in the R package mixAK. The primary function which invokes the RJ-MCMC algorithm is NMixMCMC and results in an object which contains sampled values and several basic posterior summary statistics.
