Steps to implement Bayesian input distribution selection

There are known pragmatic and theoretical difficulties associated with some standard approaches for input distribution selection for discrete-event simulations. One difficulty is a systematic underestimate of the variance of the expected simulation output that comes from not knowing the 'true' parameter values. Another is a lack of quantification of the probability that a given distribution is best. Bayesian methods have been proposed as an alternative, but acceptance has not yet been achieved, in part because of increased computational demands, as well as challenges posed by the specification of prior distributions. In this paper, we show that responses to questions like those already asked and answered in practice can be used to develop prior distributions for a wide class of models. Further, we illustrate techniques for addressing some computational difficulties thought to be associated with the implementation of Bayesian methodology.