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dc.contributor.advisorBoris Katzen_US
dc.contributor.authorRadul, Alexeyen_US
dc.contributor.otherInfolaben_US
dc.date.accessioned2007-12-19T19:15:14Z
dc.date.available2007-12-19T19:15:14Z
dc.date.issued2007-10-22en_US
dc.identifier.otherMIT-CSAIL-TR-2007-059en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/39831
dc.description.abstractReasoning with probabilistic models is a widespread andsuccessful technique in areas ranging from computer vision, to naturallanguage processing, to bioinformatics. Currently, these reasoningsystems are either coded from scratch in general-purpose languages oruse formalisms such as Bayesian networks that have limited expressivepower. In both cases, the resulting systems are difficult to modify,maintain, compose, and interoperate with. This work presents ProbabilisticScheme, an embedding of probabilistic computation into Scheme. Thisgives programmers an expressive language for implementing modularprobabilistic models that integrate naturally with the rest of Scheme.en_US
dc.format.extent9 p.en_US
dc.relationMassachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratoryen_US
dc.relationen_US
dc.subjectprobabilityen_US
dc.titleReport on the Probabilistic Language Schemeen_US


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