A Bayesian Model for the Analysis of Quantal Response Data

Abstract

Inference on the failure probabilities of ordered binomial trials conducted at M differing stress levels is considered. It is shown that a general joint prior may be constructed as a mixture of ordered M variate Dirichlet distributions, which possesses marginals of nearly arbitrary shapes. Posterior marginals at both observational and non-observational stresses are shown to consist of sums of beta distributions. Recursive relationships are developed that permit the rapid and exact computation of the posterior marginal distributions. The model is attractive for use in successive Bayesian analyses.

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Document Details

Document Type
Technical Report
Publication Date
May 05, 2003
Accession Number
ADA418744

Entities

People

  • William W. Mcdonald

Organizations

  • Naval Surface Warfare Center Indian Head Division

Tags

Communities of Interest

  • Materials and Manufacturing Processes
  • Weapons Technologies

DTIC Thesaurus Topics

  • Bayesian Networks
  • Binomials
  • Computations
  • Computer Programs
  • Computers
  • Distribution Functions
  • Engineering
  • Military Research
  • Models
  • New York
  • Operations Research
  • Pattern Recognition
  • Physics Laboratories
  • Probability
  • Statistics
  • Surface Warfare
  • Universities

Fields of Study

  • Mathematics

Readers

  • Neural Network Machine Learning.
  • Regression Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference