A Numerical Analysis of Sampling Plans Based on Prior Distribution and Costs.

Abstract

This paper deals with some pitfalls linked with the sampling model based on prior distribution and costs. First, a model is designed which encompasses most of the existing Bayesian cost models. The efficiency of sampling plans is investigated in a numerical study. It is shown that under realistic assumptions, described by Dodge (1969) and Schilling (1982), sampling plans based on prior distributions and costs are only efficient in an outlier model, i.e. if almost all lots are good quality and only a low number of lots, denoted as outlier lots, have very poor quality. Furthermore, it is demonstrated that for the Polya distribution a gain of sampling is linked with a high percentage of rejections, i.e. when the prior distribution cost relationship is such that less than 5% of the lots should be rejected sampling becomes inefficient.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 1984
Accession Number
ADA147718

Entities

People

  • H. Schneider

Organizations

  • University of North Carolina at Chapel Hill

Tags

Communities of Interest

  • C4I
  • Human Systems

DTIC Thesaurus Topics

  • Bayesian Networks
  • Classification
  • Closed Loop Systems
  • Commerce
  • Control Systems
  • Cost Models
  • Dynamic Programming
  • Efficiency
  • Manufacturing
  • New York
  • Numerical Analysis
  • Operations Research
  • Probability
  • Production
  • Quality Control
  • Rejection
  • Sampling

Fields of Study

  • Mathematics

Readers

  • Computational Modeling and Simulation
  • Regression Analysis.
  • Statistical inference.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference