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.
CITATION STYLE
Cozman, F. G., De Campos, C. P., & Da Rocha, J. C. F. (2006). Probabilistic logic with strong independence. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4140 LNAI, pp. 612–621). Springer Verlag. https://doi.org/10.1007/11874850_65
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