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Reasoning 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.
