Probabilistic Logic with Strong Independence

  • Fabio G. Cozman
  • Cassio P. de Campos
  • José Carlos F. da Rocha
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4140)

Abstract

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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Fabio G. Cozman
    • 1
  • Cassio P. de Campos
    • 1
    • 2
  • José Carlos F. da Rocha
    • 3
  1. 1.Escola PolitécnicaUniv. de São PauloSão PauloBrazil
  2. 2.Univ. MackenzieSão PauloBrazil
  3. 3.Univ. Estadual de Ponta GrossaPonta GrossaBrazil

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