Evaluating inference algorithms for the Prolog factor language
Evaluating inference algorithms for the Prolog factor language
dc.contributor.author | Gomes,T | en |
dc.contributor.author | Vítor Santos Costa | en |
dc.date.accessioned | 2018-01-19T01:33:18Z | |
dc.date.available | 2018-01-19T01:33:18Z | |
dc.date.issued | 2013 | en |
dc.description.abstract | Over the last years there has been some interest in models that combine first-order logic and probabilistic graphical models to describe large scale domains, and in efficient ways to perform inference on these domains. Prolog Factor Language (PFL) is a extension of the Prolog language that allows a natural representation of these first-order probabilistic models (either directed or undirected). PFL is also capable of solving probabilistic queries on these models through the implementation of four inference algorithms: variable elimination, belief propagation, lifted variable elimination and lifted belief propagation. We show how these models can be easily represented using PFL and then we perform a comparative study between the different inference algorithms in four artificial problems. © 2013 Springer-Verlag. | en |
dc.identifier.uri | http://repositorio.inesctec.pt/handle/123456789/7031 | |
dc.identifier.uri | http://dx.doi.org/10.1007/978-3-642-38812-5_6 | en |
dc.language | eng | en |
dc.relation | 5129 | en |
dc.rights | info:eu-repo/semantics/openAccess | en |
dc.title | Evaluating inference algorithms for the Prolog factor language | en |
dc.type | conferenceObject | en |
dc.type | Publication | en |
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