A glimpse of symbolic-statistical modeling by PRISM

We give a brief overview of a logic-based symbolic modeling language PRISM which provides a unified approach to generative probabilistic models including Bayesian networks, hidden Markov models and probabilistic context free grammars. We include some experimental result with a probabilistic context free grammar extracted from the Penn Treebank. We also show EM learning of a probabilistic context free graph grammar as an example of exploring a new area.

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