Belief Updating and Learning in Semi-Qualitative Probabilistic Networks

C. P. de Campos, F. G. Cozman

Research output: Chapter in Book/Report/Conference proceedingConference contribution

21 Citations (Scopus)

Abstract

This paper explores semi-qualitative probabilistic networks (SQPNs) that combine numeric and qualitative information. We first show that exact inferences with SQPNs are NPPP-Complete. We then show that existing qualitative relations in SQPNs (plus probabilistic logic and imprecise assessments) can be dealt effectively through multilinear programming. We then discuss learning: we consider a maximum likelihood method that generates point estimates given a SQPN and empirical data, and we describe a Bayesian-minded method that employs the Imprecise Dirichlet Model to generate set-valued estimates.
Original languageEnglish
Title of host publicationConference on Uncertainty in Artificial Intelligence (UAI)
PublisherAUAI Press
Pages153-160
Number of pages8
Publication statusPublished - 2005

Bibliographical note

(top 9%, plenary presentation, double-blind peer reviewed by >3 reviewers)

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