Computational aspects in multistage Bayesian acceptance sampling

Abstract

Simple criteria are found for reducing the computational effort in multistage Bayesian acceptance sampling. Regions of optimality are given for both terminal actions accept and reject. Also, criteria are presented for detecting nonoptimality of sets of sample sizes. Finally, nearly optimal (z,c−,c+)‐sampling plans are constructed by restricting attention to a small subset of sample sizes.