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Random-World Semantics and Syntactic Independence for Expressive Languages

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dc.contributor.advisor Leslie Kaelbling en_US McAllester, David en_US Milch, Brian en_US Goodman, Noah D. en_US
dc.contributor.other Learning and Intelligent Systems en_US 2008-05-05T15:45:52Z 2008-05-05T15:45:52Z 2008-05-03 en_US
dc.identifier.other MIT-CSAIL-TR-2008-025 en_US
dc.description.abstract We consider three desiderata for a language combining logic and probability: logical expressivity, random-world semantics, and the existence of a useful syntactic condition for probabilistic independence. Achieving these three desiderata simultaneously is nontrivial. Expressivity can be achieved by using a formalism similar to a programming language, but standard approaches to combining programming languages with probabilities sacrifice random-world semantics. Naive approaches to restoring random-world semantics undermine syntactic independence criteria. Our main result is a syntactic independence criterion that holds for a broad class of highly expressive logics under random-world semantics. We explore various examples including Bayesian networks, probabilistic context-free grammars, and an example from Mendelian genetics. Our independence criterion supports a case-factor inference technique that reproduces both variable elimination for BNs and the inside algorithm for PCFGs. en_US
dc.format.extent 6 p. en_US
dc.relation Massachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory en_US
dc.relation en_US
dc.title Random-World Semantics and Syntactic Independence for Expressive Languages en_US

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