On the Implementation of the CLP( $\mathcal BN$ ) Language

Costa, Vítor Santos

doi:10.1007/978-3-642-11503-5_20

Search citation statements

Order By: Relevance

Paper Sections

Select...

Inference In the Pfl1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2013

Publication Types

Select...

Book1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 2 publications

(1 citation statement)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The PFL uses the convention that in Bayesian networks a single parfactor defines each variable, and in markov networks all parfactors define the variable. This implementation is currently used by variable elimination, belief propagation and counting belief propagation, and the CLP(BN ) solvers [25].…”

Section: Inference In the Pflmentioning

confidence: 99%

Evaluating Inference Algorithms for the Prolog Factor Language

Gomes

Costa

2013

Inductive Logic Programming

View full text Add to dashboard Buy / Rent full text

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.

show abstract

Section: Inference In the Pflmentioning

confidence: 99%