Belief Updating and Learning in Semi-Qualitative Probabilistic Networks
Cassio de Campos, Fabio Cozman
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.
PDF Link: /papers/05/p153-de_campos.pdf
AUTHOR = "Cassio de Campos
and Fabio Cozman",
TITLE = "Belief Updating and Learning in Semi-Qualitative Probabilistic Networks",
BOOKTITLE = "Proceedings of the Twenty-First Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-05)",
PUBLISHER = "AUAI Press",
ADDRESS = "Arlington, Virginia",
YEAR = "2005",
PAGES = "153--160"