Journal Article10.2307/2348442
Some comments on Bayesian sample size determination
TL;DR: In this paper, the authors review several properties of sample size methods and discuss the importance of these properties in the context of a binomial experiment, and present a general algorithm for Bayesian sample size determination that is useful for more complex sampling situations based on Monte Carlo simulations.
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Abstract: SUMMARY Several criteria for Bayesian sample size determination have recently been proposed. Criteria based on highest posterior density (HPD) intervals from the exact posterior distribution in general lead to smaller sample sizes than those based on non-HPD intervals and/or normal approximations to the exact density. The economies are variable, however, and depend both on the prior inputs and the desired posterior accuracy and coverage probability. In our reply we review several properties of sample size methods and discuss the importance of these properties in the context of a binomial experiment. A general algorithm for Bayesian sample size determination that is useful for more complex sampling situations based on Monte Carlo simulations is briefly described.
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- 01 Jan 1987
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