Abstract

The problem of selecting the best system from a finite set of alternatives is considered from a Bayesian decision-theoretic perspective. The framework presented is quite general, and permits selection from two or more systems, with replications that use either independent or common random numbers, with unknown means and covariances for the output, and permits Gaussian or non-Gaussian simulation output. For the case of unknown mean and variance with common random numbers, the framework provides a probability of correct selection that does not suffer from problems associated with the Bonferroni inequality. We indicate some criteria for which the Bayesian approach and other approaches are in general agreement, or disagreement. The probability of correct selection can be calculated either by quadrature or by Monte Carlo simulation from the posterior distribution of the parameters of the statistical distribution of the simulation output. We also comment on expected-value decision-making ver...

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