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arXiv · 1207.1367

Belief Updating and Learning in Semi-Qualitative Probabilistic Networks

Cassio Polpo de Campos·Fabio Gagliardi Cozman
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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.

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Cassio Polpo de Campos, Fabio Gagliardi Cozman. 2012-07-04. Belief Updating and Learning in Semi-Qualitative Probabilistic Networks. https://arxiv.org/abs/1207.1367

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