A GENERALIZATION OF BAYESIAN INFERENCE

Abstract

Procedures of statistical inference are described which generalize Bayesian inference in specific ways. Probability is used in such a way that in general only bounds may be placed on the probabilities of given events, and probability systems of this kind are suggested both for sample information and for prior information. These systems are then combined using a specified rule. Illustrations are given for inferences about trinomial probabilities, and for inferences about a monotone sequence of binomial P sub i. Finally, some comments are made on the general class of models which produce upper and lower probabilities, and on the specific models which underlie the suggested inference procedures.

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Document Details

Document Type
Technical Report
Publication Date
Nov 15, 1967
Accession Number
AD0664659

Entities

People

  • A. P. Dempster

Organizations

  • Harvard University

Tags

Communities of Interest

  • C4I

DTIC Thesaurus Topics

  • Bayesian Inference
  • Binomials
  • Commerce
  • Data Science
  • Engineering
  • Information Science
  • Mathematical Models
  • Mathematics
  • Military Research
  • New York
  • Probability
  • Probability Distributions
  • Sequences
  • Statistical Analysis
  • Statistical Inference
  • Statistical Samples
  • Statistics

Fields of Study

  • Mathematics

Readers

  • Regression Analysis.

Technology Areas

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