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Open Access Publications from the University of California

Discrete Bayes with R


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

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