Bayesian Assessment of the Distribution of Insurance Claim Counts Using Reversible Jump MCMC

Abstract

The aim of this paper is to construct Bayesian model comparison tests between discrete distributions used for claim count modeling in the actuarial field. We use advanced computational techniques to estimate the posterior model odds among different distributions for claim counts. We construct flexible reversible jump Markov Chain Monte Carlo algorithms and implement them in various illustrated examples.