Structure and interpretation of computer programs
Structure and interpretation of computer programs
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Revised5 report on the algorithmic language scheme
ACM SIGPLAN Notices
Probabilistic frame-based systems
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Stochastic lambda calculus and monads of probability distributions
POPL '02 Proceedings of the 29th ACM SIGPLAN-SIGACT symposium on Principles of programming languages
Learning Probabilistic Relational Models
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
IBAL: a probabilistic rational programming language
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
BLOG: probabilistic models with unknown objects
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Monolingual probabilistic programming using generalized coroutines
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
A model-learner pattern for bayesian reasoning
POPL '13 Proceedings of the 40th annual ACM SIGPLAN-SIGACT symposium on Principles of programming languages
Proceedings of the 34th ACM SIGPLAN conference on Programming language design and implementation
Dynamic enforcement of knowledge-based security policies using probabilistic abstract interpretation
Journal of Computer Security
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Reasoning with probabilistic models is a widespread and successful technique in areas ranging from computer vision, to natural language processing, to bioinformatics. Currently, these reasoning systems are either coded from scratch in general-purpose languages or use formalisms such as Bayesian networks that have limited expressive power. In both cases, the resulting systems are difficult to modify, maintain, compose, and interoperate with. This work presents Probabilistic Scheme, an embedding of probabilistic computation into Scheme. This gives programmers an expressive language for implementing modular probabilistic models that integrate naturally with the rest of Scheme.