A guide to simulation (2nd ed.)
A guide to simulation (2nd ed.)
Probabilistic non-determinism
Stochastic processes as concurrent constraint programs
Proceedings of the 26th ACM SIGPLAN-SIGACT symposium on Principles of programming languages
Monte Carlo localization: efficient position estimation for mobile robots
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Intersection types and computational effects
ICFP '00 Proceedings of the fifth ACM SIGPLAN international conference on Functional programming
A modal analysis of staged computation
Journal of the ACM (JACM)
Stochastic lambda calculus and monads of probability distributions
POPL '02 Proceedings of the 29th ACM SIGPLAN-SIGACT symposium on Principles of programming languages
Toward General Analysis of Recursive Probability Models
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
A judgmental reconstruction of modal logic
Mathematical Structures in Computer Science
The call-by-need lambda calculus
Journal of Functional Programming
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
A probabilistic language based upon sampling functions
Proceedings of the 32nd ACM SIGPLAN-SIGACT symposium on Principles of programming languages
A probabilistic language based on sampling functions
ACM Transactions on Programming Languages and Systems (TOPLAS)
Hi-index | 0.00 |
As probabilistic computation plays an increasing role in diverse fields in computer science, researchers have designed new languages to facilitate the development of probabilistic programs. In this paper, we develop a probabilistic calculus by extending the traditional lambda calculus. In our calculus, every expression denotes a probability distribution yet evaluates to a regular value. The most notable feature of our calculus is that it is founded upon sampling functions, which map the unit interval to probability domains. As a consequence, we achieve a unified representation scheme for all types of probability distributions. In order to support an efficient implementation of the calculus, we also develop a refinement type system which is capable of distinguishing expressions denoting regular values from expressions denoting probability distributions. We use a novel formulation of the intuitionistic modal logic S4 with an intersection connective in the refinement type system. We present preliminary evidence that a probabilistic language based upon our calculus is viable in applications involving massive probabilistic computation.