TY - GEN
T1 - Probabilistic logic with strong independence
AU - Cozman, Fabio G.
AU - De Campos, Cassio P.
AU - Da Rocha, José Carlos F.
PY - 2006/1/1
Y1 - 2006/1/1
N2 - 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.
AB - 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.
UR - https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=33751371479&origin=inward
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U2 - 10.1007/11874850_65
DO - 10.1007/11874850_65
M3 - Conference contribution
SN - 3540454624
SN - 9783540454625
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 612
EP - 621
BT - Probabilistic logic with strong independence
T2 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Y2 - 1 January 2018
ER -