Probabilistic logic with strong independence
Tipo
Artigo de evento
Data de publicação
2006
Periódico
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Citações (Scopus)
0
Autores
Cozman F.G.
De Campos C.P.
Da Rocha J.C.F.
De Campos C.P.
Da Rocha J.C.F.
Orientador
Título da Revista
ISSN da Revista
Título de Volume
Membros da banca
Programa
Resumo
This papers investigates the manipulation of statements of strong independence in probabilistic logic. Inference methods based on polynomial programming are presented for strong independence, both for unconditional and conditional cases. We also consider graph-theoretic representations, where each node in a graph is associated with a Boolean variable and edges carry a Markov condition. The resulting model generalizes Bayesian networks, allowing probabilistic assessments and logical constraints to be mixed. © Springer-Verlag Berlin Heidelberg 2006.
Descrição
Palavras-chave
Assuntos Scopus
Bayesian networks , Conditional cases , Polynomial programming