An Empirical Comparison of Bayesian and Credal Networks for Dependable High-Level Information Fusion
2008 (English)In: Proceedings of the 11th International Conference on Information Fusion: Cologne, Germany, June 30 - July 03, 2008, IEEE, 2008, p. 1359-1366Conference paper, Published paper (Refereed)
Abstract [en]
Bayesian networks are often proposed as a method for high-level information fusion. However, a Bayesian network relies on strong assumptions about the underlying probabilities. In many cases it is not realistic to require such precise probability assessments. We show that there exists a significant set of problems where credal networks outperform Bayesian networks, thus enabling more dependable decision making for this type of problems. A credal network is a graphical probabilistic method that utilizes sets of probability distributions, e.g., interval probabilities, for representation of belief. Such a representation allows one to properly express epistemic uncertainty, i.e., uncertainty that can be reduced if more information becomes available. Since reducing uncertainty has been proposed as one of the main goals of information fusion, the ability to represent epistemic uncertainty becomes an important aspect in all fusion applications.
Place, publisher, year, edition, pages
IEEE, 2008. p. 1359-1366
Keywords [en]
High-level information fusion, credal networks, Bayesian networks, dependability, epistemic uncertainty, imprecise probability
Research subject
Technology
Identifiers
URN: urn:nbn:se:his:diva-3617Scopus ID: 2-s2.0-56749148274ISBN: 978-3-00-024883-2 (electronic)ISBN: 978-3-8007-3092-6 (print)OAI: oai:DiVA.org:his-3617DiVA, id: diva2:291270
Conference
11th International Conference on Information Fusion, FUSION 2008, Cologne, 30 June 2008 through 3 July 2008, IEEE catalog number CFP08FUS, INSPEC Accession Number: 10365860, Code 74110
Note
10.1109/ICIF.2008.4632369
2010-02-012010-02-012020-08-11Bibliographically approved