Uncertain Evidence and Artificial Analysis.

Abstract

Belief function models form a natural basis for the construction of artificial analysts (eg, expert systems) capable of uncertain judgments. Random samples of various types may be used as the uncertain evidence which defines the necessary probability structure for the model. The paper develops some simple examples to illustrate these ideas. (Author)

Document Details

Document Type
Technical Report
Publication Date
Mar 16, 1987
Accession Number
ADP005292

Entities

People

  • A. P. Dempster
  • Augustine Kong

Organizations

  • Harvard University

Tags

DTIC Thesaurus Topics

  • Bayes Theorem
  • California
  • Construction
  • Data Science
  • Expert Systems
  • Information Science
  • Judgment
  • Mathematical Analysis
  • Mathematics
  • Probability
  • Statistical Samples
  • Theorems
  • Uncertainty
  • Workshops

Fields of Study

  • Mathematics

Readers

  • Geospatial Intelligence and Artificial Intelligence Analytics
  • Theoretical Analysis.