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 form Bayesian updating on uncertain evidence.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1985
Accession Number
ADA250538

Entities

People

  • Henry Kyburg Jr.

Organizations

  • University of Rochester

Tags

Communities of Interest

  • Biomedical
  • Energy and Power Technologies
  • Human Systems

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Bayesian Inference
  • Bayesian Networks
  • Computer Science
  • Convex Sets
  • Data Science
  • Information Processing
  • Information Science
  • Intervals
  • Mathematics
  • Probability
  • Probability Distributions
  • Statistical Algorithms
  • Statistical Analysis
  • 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