Bayesian and Non-Bayesian Evidential Updating

Abstract

Four main results are arrived at in this paper. (1) Closed convex sets of classical probability functions provide a representation of belief that includes the representations provided by Shafer probability mass functions as a special case. (2) The impact of uncertain evidence can be (formally) represented by Dempster conditioning, in Shafer's framework. (3) The impact of uncertain evidence can be (formally) represented in the framework of convex sets of classical probabilities by classical conditionalization. (4) The probability intervals that result from Dempster/Shafer updating on uncertain evidence are included in (and may be properly included in) the intervals that result for Bayesian updating on uncertain evidence.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1984
Accession Number
ADA250345

Entities

People

  • Henry E. Kyburg Jr.

Organizations

  • University of Rochester

Tags

Communities of Interest

  • Energy and Power Technologies
  • Human Systems

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Bayesian Inference
  • Bayesian Networks
  • Computer Science
  • Computers
  • Convex Sets
  • Data Science
  • Expert Systems
  • Frequency
  • Information Science
  • Probability
  • Probability Distributions
  • Statistical Algorithms
  • Statistical Inference
  • Statistics
  • Theorems

Fields of Study

  • Mathematics

Readers

  • Artificial Intelligence
  • Statistical inference.

Technology Areas

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