Bayesian Analysis of Insurance Losses Using the Buhlmann-Straub Credibility Model

We propose a Bayesian analysis to develop credibility estimates of the well known Biihlmann-Straub model. We describe simple numerical methods to obtain exact posterior distributions and predictive densities under this model. These distributions are obtained through Monte Carlo simulations that generate independent samples from the joint posterior distribution. Our methods are therefore preferable to methods such as Gibbs sampling, which generates dependent samples from the joint distribution. The methods discussed also can be extended to more complicated credibility models