Bayesian Approach to Single Variable Sampling Plans

We develop a model of sampling plans for variables with a polynomial loss function, in which the decision function is either one-sided or two- sided. Based on a Bayesian approach, we suggest a simple finite algorithm for the determination of the optimal single sampling plan. Furthermore, for the case of a symmetric two-sided decision function, we propose an approximate method for determining its optimal single sampling plan.

Related Organizations

Chinese University of Hong Kong
China (People's Republic of)

Keywords

polynomial loss function, Bayes risk, decision function, symmetric two-sided decision function, Applications of statistics in engineering and industry; control charts, approximate method, Bayesian problems; characterization of Bayes procedures, Hermite polynomial, sampling inspection by variables, simple finite algorithm, optimal single sampling plan

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