Thirty years of credal networks: Specification, algorithms and complexity

Denis Deratani Mauá, Fabio Gagliardi Cozman

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

Credal networks generalize Bayesian networks to allow for imprecision in probability values. This paper reviews the main results on credal networks under strong independence, as there has been significant progress in the literature during the last decade or so. We focus on computational aspects, summarizing the main algorithms and complexity results for inference and decision making. We address the question “What is really known about strong extensions of credal networks?” by looking at theoretical results and by presenting a short summary of real applications.

Original languageEnglish
Pages (from-to)133-157
Number of pages25
JournalInternational Journal of Approximate Reasoning
Volume126
DOIs
StatePublished - Nov 2020
Externally publishedYes

Keywords

  • Imprecise probabilities
  • Probabilistic graphical models

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