Abstract
Multistage Bayesian decision procedures in statistical quality control are known from attribute sampling. In this paper they are introduced in a more general framework occuring in lot-control by using the theory of Bayesian sequentially planned decision procedures. We show that under sufficiency and transitivity assumptions and monotonicity properties concerning the distribution and cost set-up these Bayes-procedures have (z, c −,c +)-structure which, on one hand, generalizes results of K.-H. Waldmann and, on the other hand, reduces computational effort significantly. Finally, examples taken from attribute sampling and life testing for an outgoing lot are presented.
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The theory presented in this paper is taken from [11].
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Terveer, I. Cost-optimal multistage sampling plans in statistical quality control. ZOR - Methods and Models of Operations Research 41, 359–380 (1995). https://doi.org/10.1007/BF01432365
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DOI: https://doi.org/10.1007/BF01432365