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We discuss the application of the Bayesian statistical paradigm in conjunction with Monte Carlo methods to practical problems. We begin by describing the basic constructs of the Bayesian paradigm. We then discuss two applications. The first entails the simulation of a two-stage model of a property-casualty insurance operation. The second application(More)
We fit a linear mixed model and a Bayesian hierarchical model to data provided by an insurance company located in the Midwest. We used models fit to the 1994 data to predict health insurance claims costs for 1995. We implemented the linear mixed model in SAS and used two different prediction methods to predict 1995 costs. In the linear mixed model we(More)
In an earlier paper [2], a "best" procedure was presented for generating pseudorandom normal deviates in APL° Here a method is proposed for generating uniform pseudorandom numbers in APL. The APL primitive function Roll (denoted by ~?') is a m u l t i p l i c a t i v e congruential generator implemented in a fashion equivalent to: Thus, after each(More)