Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
A model for reasoning about persistence and causation
Computational Intelligence
Probabilistic Horn abduction and Bayesian networks
Artificial Intelligence
Decision-theoretic troubleshooting
Communications of the ACM
Generalized queries on probabilistic context-free grammars
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Object-oriented Bayesian networks
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Context-specific independence in Bayesian networks
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Bucket elimination: a unifying framework for probabilistic inference
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Generalized Queries on Probabilistic Context-Free Grammars
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stochastic processes as concurrent constraint programs
Proceedings of the 26th ACM SIGPLAN-SIGACT symposium on Principles of programming languages
Stochastic lambda calculus and monads of probability distributions
POPL '02 Proceedings of the 29th ACM SIGPLAN-SIGACT symposium on Principles of programming languages
Annals of Software Engineering
A calculus for probabilistic languages
Proceedings of the 2003 ACM SIGPLAN international workshop on Types in languages design and implementation
Statistical Abduction with Tabulation
Computational Logic: Logic Programming and Beyond, Essays in Honour of Robert A. Kowalski, Part II
World-Modeling vs. World-Axiomatizing
LPNMR '99 Proceedings of the 5th International Conference on Logic Programming and Nonmonotonic Reasoning
Efficient EM Learning with Tabulation for Parameterized Logic Programs
CL '00 Proceedings of the First International Conference on Computational Logic
Reconstructing force-dynamic models from video sequences
Artificial Intelligence
Computational Linguistics
A probabilistic language based upon sampling functions
Proceedings of the 32nd ACM SIGPLAN-SIGACT symposium on Principles of programming languages
PRL: A probabilistic relational language
Machine Learning
A probabilistic language based on sampling functions
ACM Transactions on Programming Languages and Systems (TOPLAS)
Embedded Probabilistic Programming
DSL '09 Proceedings of the IFIP TC 2 Working Conference on Domain-Specific Languages
Purely functional lazy non-deterministic programming
Proceedings of the 14th ACM SIGPLAN international conference on Functional programming
Preprocessing for Optimization of Probabilistic-Logic Models for Sequence Analysis
ICLP '09 Proceedings of the 25th International Conference on Logic Programming
Parameter learning of logic programs for symbolic-statistical modeling
Journal of Artificial Intelligence Research
IBAL: a probabilistic rational programming language
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Monolingual probabilistic programming using generalized coroutines
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
P-CLASSIC: a tractable probablistic description logic
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Measure transformer semantics for Bayesian machine learning
ESOP'11/ETAPS'11 Proceedings of the 20th European conference on Programming languages and systems: part of the joint European conferences on theory and practice of software
Program analysis and machine learning: a win-win deal
SAS'11 Proceedings of the 18th international conference on Static analysis
From Bayesian notation to pure racket via discrete measure-theoretic probability in λZFC
IFL'10 Proceedings of the 22nd international conference on Implementation and application of functional languages
Loglinear models for first-order probabilistic reasoning
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Reduction of maximum entropy models to hidden markov models
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Probabilistic state-dependent grammars for plan recognition
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
Toward general analysis of recursive probability models
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
Object-oriented Bayesian networks
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Purely functional lazy nondeterministic programming
Journal of Functional Programming - Dedicated to ICFP 2009
Program analysis and machine learning: a win-win deal
APLAS'11 Proceedings of the 9th Asian conference on Programming Languages and Systems
Bayesian network automata for modelling unbounded structures
IWPT '11 Proceedings of the 12th International Conference on Parsing Technologies
A model-learner pattern for bayesian reasoning
POPL '13 Proceedings of the 40th annual ACM SIGPLAN-SIGACT symposium on Principles of programming languages
Bayesian inference using data flow analysis
Proceedings of the 2013 9th Joint Meeting on Foundations of Software Engineering
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In this paper, we propose a stochastic version of a general purpose functional programming language as a method of modeling stochastic processes. The language contains random choices, conditional statements, structured values, defined functions, and recursion. By imagining an experiment in which the program is "run" and the random choices made by sampling, we can interpret a program in this language as encoding a probability distribution over a (potentially infinite) set of objects. We provide an exact algorithm for computing conditional probabilities of the form Pr(P(x) | Q(x)) where x is chosen randomly from this distribution. This algorithm terminates precisely when sampling x and computing P(x) and Q(x) terminates in all possible stochastic executions (under lazy evaluation semantics, in which only values needed to compute the output of the program are evaluated). We demonstrate the applicability of the language and the efficiency of the inference algorithm by encoding both Bayesian networks and stochastic context-free grammars in our language, and showing that our algorithm derives efficient inference algorithms for both. Our language easily supports interesting and useful extensions to these formalisms (e.g., recursive Bayesian networks), to which our inference algorithm will automatically apply.