On the Problem of Selecting Good Populations.

Abstract

The problem of selecting good populations out of k normal populations is considered in a Bayesian framework under exchangeable normal priors and additive loss functions. Some basic approximations to the Bayes rules are discussed. These approximations suggest that some well-known classical rules are 'approximate' Bayes rules. Especially, it is shown that Gupta-type rules are extended Bayes with respect to a family of the exchangeable normal priors for any bounded and additive loss function. Furthermore, for a simple loss function, the results of a Monte Carlo comparison of Gupta-type rules and Seal-type rules are presented. They indicate that, in general, Gupta-type rules perform better than Seal-type rules. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Aug 01, 1980
Accession Number
ADA100947

Entities

People

  • Woo-chul Kim

Organizations

  • Purdue University

Tags

Communities of Interest

  • C4I
  • Human Systems

DTIC Thesaurus Topics

  • Additives (Chemicals)
  • Bayesian Networks
  • Computational Science
  • Distribution Functions
  • Families (Human)
  • Military Research
  • New York
  • Normal Distribution
  • North Carolina
  • Probabilistic Models
  • Probability
  • Probability Distributions
  • Random Variables
  • Simulations
  • Standards
  • Statistics
  • United States

Fields of Study

  • Mathematics

Readers

  • Statistical inference.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms