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Parameter Learning of Logic Programs for Symbolic-Statistical Modeling
2001
The Journal of Artificial Intelligence Research
We propose a logical/mathematical framework for statistical parameter learning of parameterized logic programs, i.e. definite clause programs containing probabilistic facts with a parameterized distribution. It extends the traditional least Herbrand model semantics in logic programming to distribution semantics, possible world semantics with a probability distribution which is unconditionally applicable to arbitrary logic programs including ones for HMMs, PCFGs and Bayesian networks. We also
doi:10.1613/jair.912
fatcat:abqliz2yxrbi3d5e6zeoh3tqd4