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 language||American English|
|Number of pages||8|
|State||Published - 1 Dec 2005|
|Event||Proceedings of the 21st Conference on Uncertainty in Artificial Intelligence, UAI 2005 - |
Duration: 1 Dec 2005 → …
|Conference||Proceedings of the 21st Conference on Uncertainty in Artificial Intelligence, UAI 2005|
|Period||1/12/05 → …|