Adaptive Bayesian Logic Programs
Lecture notes in computer sciencePublished 1 January 2001Open access
Kristian Kersting, Luc De Raedt
Citations122
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TL;DR
Continuous Bayesian logic programs are introduced, which extend the recently introduced Bayesian Logic programs to deal with continuous random variables and nicely seperates the qualitative component from the quantitative one.
Abstract
© Springer-Verlag Berlin Heidelberg 2001. First order probabilistic logics combine a first order logic with a probabilistic knowledge representation. In this context, we introduce continuous Bayesian logic programs, which extend the recently introduced Bayesian logic programs to deal with continuous random variables. Bayesian logic programs tightly integrate definite logic programs with Bayesian networks. The resulting framework nicely seperates the qualitative (i.e. logical) component from the quantitative (i.e. the probabilistic) one. We also show how the quantitative component can be learned using a gradient-based maximum likelihood method.
Keywords
Computer Science
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