Hierarchical Bayesian Selection Procedures for the Best Binomial Population

Abstract

In this paper a hierarchical Bayesian model is adopted to derive selection procedures for selecting the best of k binomial parameters, say the probability of success corresponding to k different suppliers. This model facilitates the use of prior information in the analysis for both small and large sample sizes. In addition to computing posterior probabilities that the i to the th power supplier is best, this paper presents expressions for deciding how much better a given supplier is relative to the others. Prior information is assumed to begin with exchangeability and can be more informative if the experimenter has other knowledge about the suppliers as a group. A numerical example is given and the paper concludes with remarks about future work.

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Document Details

Document Type
Technical Report
Publication Date
May 01, 1988
Accession Number
ADA196651

Entities

People

  • John J. Deely
  • Shanti Gupta

Organizations

  • Purdue University

Tags

Communities of Interest

  • Energy and Power Technologies
  • Human Systems

DTIC Thesaurus Topics

  • Bayesian Inference
  • Bayesian Networks
  • Binomials
  • Classification
  • Computational Science
  • Computations
  • Data Science
  • Decision Theory
  • Estimators
  • Information Science
  • Models
  • New York
  • Numerical Integration
  • Probability
  • Security
  • Statistical Decision Theory
  • Statistics

Fields of Study

  • Mathematics

Readers

  • Defense Technology Research and Development.
  • Statistical inference.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - DoD AI Strategy