A Bayes Procedure for Selecting the Population with the Largest pth Quantile.

Abstract

The Bayesian approach has not been very fruitful in treating nonparametric statistical problems, due to the difficulty in finding mathematically tractable prior distributions on a set of probability measures. The theory of the Dirichlet process has been developed recently. The process generates randomly a family of probability distributions which can be taken as a family of prior distributions for the Bayesian analysis of some nonparametric statistical problems. This paper deals with the problem of selection a distribution with the largest pth quantile value, from k > or = 2 given distributions. It is assumed a priori that the given distributions have been generated from a Dirichlet process.

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 1981
Accession Number
ADA112947

Entities

People

  • Khursheed Alam

Organizations

  • Clemson University

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Bayesian Networks
  • Contracts
  • Distribution Functions
  • Estimators
  • Military Research
  • Probabilistic Models
  • Probability
  • Probability Distributions
  • Random Variables
  • Security
  • South Carolina
  • Stochastic Processes
  • Universities

Fields of Study

  • Mathematics

Readers

  • Neural Network Machine Learning.
  • Regression Analysis.
  • Theoretical Analysis.

Technology Areas

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