A guide to simulation (2nd ed.)
A guide to simulation (2nd ed.)
Real and complex analysis, 3rd ed.
Real and complex analysis, 3rd ed.
Computational lambda-calculus and monads
Proceedings of the Fourth Annual Symposium on Logic in computer science
Notions of computation and monads
Information and Computation
Probabilistic non-determinism
Imperative functional programming
POPL '93 Proceedings of the 20th ACM SIGPLAN-SIGACT symposium on Principles of programming languages
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Randomized algorithms
Lisp and Symbolic Computation - Special issue on state in programming languages (part I)
ACM Transactions on Programming Languages and Systems (TOPLAS)
Statistical methods for speech recognition
Statistical methods for speech recognition
Stochastic processes as concurrent constraint programs
Proceedings of the 26th ACM SIGPLAN-SIGACT symposium on Principles of programming languages
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Introduction to Monte Carlo methods
Learning in graphical models
Stochastic lambda calculus and monads of probability distributions
POPL '02 Proceedings of the 29th ACM SIGPLAN-SIGACT symposium on Principles of programming languages
Statistical Language Learning
A calculus for probabilistic languages
Proceedings of the 2003 ACM SIGPLAN international workshop on Types in languages design and implementation
Roll: A Language for Specifying Die-Rolls
PADL '03 Proceedings of the 5th International Symposium on Practical Aspects of Declarative Languages
Toward General Analysis of Recursive Probability Models
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Exploring artificial intelligence in the new millennium
An Introduction to the Kalman Filter
An Introduction to the Kalman Filter
A judgmental reconstruction of modal logic
Mathematical Structures in Computer Science
Journal of Functional Programming
Fastslam: a factored solution to the simultaneous localization and mapping problem with unknown data association
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
Computable Exchangeable Sequences Have Computable de Finetti Measures
CiE '09 Proceedings of the 5th Conference on Computability in Europe: Mathematical Theory and Computational Practice
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
Dynamic symbolic computation for domain-specific language implementation
LOPSTR'11 Proceedings of the 21st international conference on Logic-Based Program Synthesis and Transformation
On coinductive equivalences for higher-order probabilistic functional programs
Proceedings of the 41st ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages
Dynamic enforcement of knowledge-based security policies using probabilistic abstract interpretation
Journal of Computer Security
| Hi-index | 0.00 |
As probabilistic computations play an increasing role in solving various problems, researchers have designed probabilistic languages which treat probability distributions as primitive datatypes. Most probabilistic languages, however, focus only on discrete distributions and have limited expressive power. This article presents a probabilistic language, called λ○, whose expressive power is beyond discrete distributions. Rich expressiveness of λ○ is due to its use of sampling functions, that is, mappings from the unit interval (0.0,1.0] to probability domains, in specifying probability distributions. As such, λ○ enables programmers to formally express and reason about sampling methods developed in simulation theory. The use of λ○ is demonstrated with three applications in robotics: robot localization, people tracking, and robotic mapping. All experiments have been carried out with real robots.