A glimpse of symbolic-statistical modeling by PRISM |
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| Authors: | Taisuke Sato |
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| Affiliation: | (1) Tokyo Institute of Technology, Ookayama Meguro, Tokyo, Japan |
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| Abstract: | 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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| Keywords: | Symbolic-statistical modeling PRISM Probabilistic context free grammar |
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