Algebra of Dempster-Shafer Evidence Accumulation

Abstract

In this work we focus on the relationship between the Dempster-Shafer (DS) and Bayesian evidence accumulation. While it is accepted that the DS theory is, in a certain sense, a generalization of the probability theory, the approaches vary in several important respects, including the treatment of uncertain information and the way the evidence is combined, making direct comparison of results of the two analyses difficult. In this work we ameliorate these difficulties by proposing a mathematical framework within which the relationship between the two methods can be made precise. The findings of the investigation elucidate the role uncertainty plays in the DS theory and enable evaluation of relative fitness of the two techniques for practical data fusion scenarios.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2009
Accession Number
AD1107450

Entities

People

  • Andrzej K. Brodzik
  • Michael R. Pellegrini
  • Robert H. Enders

Organizations

  • MITRE Corporation

Tags

Communities of Interest

  • Energy and Power Technologies
  • Human Systems
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Artificial Neural Networks
  • Bayesian Inference
  • Classification
  • Computational Complexity
  • Computations
  • Convergence
  • Data Fusion
  • Data Science
  • Detection
  • Earth Sciences
  • Engineering
  • Identities
  • Information Science
  • Information Systems
  • Information Theory
  • Probability
  • Reasoning
  • Remote Sensing
  • Standards
  • Uncertainty

Fields of Study

  • Mathematics

Readers

  • Distributed Systems and Data Platform Development
  • Neurotoxicology
  • Theoretical Analysis.

Technology Areas

  • AI & ML