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Real and complex analysis, 3rd ed.
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Probabilistic reasoning in intelligent systems: networks of plausible inference
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Why functional programming matters
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A probabilistic powerdomain of evaluations
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Compiling with continuations
The essence of functional programming
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Probabilistic non-determinism
Arithmetic coding for data compression
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Type-directed partial evaluation
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Stochastic processes as concurrent constraint programs
Proceedings of the 26th ACM SIGPLAN-SIGACT symposium on Principles of programming languages
Learning in graphical models
QuickCheck: a lightweight tool for random testing of Haskell programs
ICFP '00 Proceedings of the fifth ACM SIGPLAN international conference on Functional programming
Statistical Language Learning
Introduction to Bayesian Networks
Introduction to Bayesian Networks
Literate Programming Simplified
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Power Domains and Predicate Transformers: A Topological View
Proceedings of the 10th Colloquium on Automata, Languages and Programming
Semantics and Inference for Recursive Probability Models
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
The Design of a Pretty-printing Library
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Variational probabilistic inference and the QMR-DT network
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IBAL: a probabilistic rational programming language
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Effective Bayesian inference for stochastic programs
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Constructing situation specific belief networks
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Bucket elimination: a unifying framework for probabilistic inference
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A calculus for probabilistic languages
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FUNCTIONAL PEARLS: Probabilistic functional programming in Haskell
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Declarative programming for artificial intelligence applications
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Report on the probabilistic language scheme
Proceedings of the 2007 symposium on Dynamic languages
A probabilistic language based on sampling functions
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Proofs of randomized algorithms in Coq
Science of Computer Programming
Probabilistic modelling, inference and learning using logical theories
Annals of Mathematics and Artificial Intelligence
The Computational SLR: A Logic for Reasoning about Computational Indistinguishability
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DSL '09 Proceedings of the IFIP TC 2 Working Conference on Domain-Specific Languages
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Probability distributions are useful for expressing the meanings of probabilistic languages, which support formal modeling of and reasoning about uncertainty. Probability distributions form a monad, and the monadic definition leads to a simple, natural semantics for a stochastic lambda calculus, as well as simple, clean implementations of common queries. But the monadic implementation of the expectation query can be much less efficient than current best practices in probabilistic modeling. We therefore present a language of measure terms, which can not only denote discrete probability distributions but can also support the best known modeling techniques. We give a translation of stochastic lambda calculus into measure terms. Whether one translates into the probability monad or into measure terms, the results of the translations denote the same probability distribution.