Discrete Bayes with R

An attractive way of introducing Bayesian thinking is through a discrete model approach<br>where the parameter is assigned a discrete prior. Two generic R functions are introduced for<br>implementing posterior and predictive calculations for arbitrary choices of prior and sampling<br>densities. Several examples illustrate the usefulness of these functions in summarizing the<br>posterior distributions for one and two parameter problems and for comparing models by the use<br>of Bayes factors