Report on the probabilistic language scheme

Proceedings of the Conference on Object-Oriented Programming Systems, Languages, and Applications, OOPSLA

Volumn , Issue , 2007, Pages 2-10

  Radul, Alexey ^a  

^a MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

Embedding; Probability; Scheme

Indexed keywords

PROBABILISTIC COMPUTATION; PROBABILISTIC MODELS; REASONING SYSTEMS;

BAYESIAN NETWORKS; BIOINFORMATICS; COMPUTER VISION; NATURAL LANGUAGE PROCESSING SYSTEMS;

PROBABILISTIC LOGICS;

EID: 41149138927     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1297081.1297085     Document Type: Conference Paper

References (15)

1. H. Abelson, G. J. Sussman, and J. Sussman. Structure and Interpretation of Computer Programs. MIT Press, 1984.
2. F. Bacchus, S. Dalmao, and T. Pitassi. Value elimination: Bayesian inference via backtracking search, 2003.
3. N. Friedman, L. Getoor, D. Koller, and A. Pfeffer. Learning probabilistic relational models. Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, pages 1300-1309, 1999.
4. R. Kelsey, W. Clinger, J. Rees, et al. Revised5 Report on the Algorithmic Language Scheme. SIGPLAN Notices, 33(9):26-76, 1998.
5. D. Koller and A. Pfeffer. Probabilistic frame-based systems. Proc. AAAI, 98, 1998.
6. J. McCarthy. A Basis for a Mathematical Theory of Computation. In P. Braffort and D. Hirschberg, editors, Computer Programming and Formal Systems. North-Holland, Amsterdam, 1963.
7. B. Milch, B. Marthi, S. Russell, D. Sontag, D. Ong, and A. Kolobov. BLOG: Probabilistic models with unknown objects. Proceedings of the Nineteenth Joint Conference on Artificial Intelligence, 2005.
8. A. Pfeffer. IBAL: A probabilistic rational programming language. In IJCAI, pages 733-740, 2001.
9. N. Ramsey and A. Pfeffer. Stochastic lambda calculus and monads of probability distributions. Proceedings of the 29th ACM SIGPLAN-SIGACT symposium on Principles of programming languages, pages 154-165, 2002.
10. S. Russell and P. Norvig. Artificial intelligence: a modern approach. Prentice-Hall, Inc. Upper Saddle River, NJ, USA, 1995.
11. M. Sahami, S. Dumais, D. Heckerman, and E. Horvitz. A Bayesian approach to filtering junk e-mail. Learning for Text Categorization: Papers from the 1998 Workshop, 62, 1998.
12. E. Segal, B. Taskar, A. Gasch, N. Friedman, and D. Koller. Rich probabilistic models for gene expression. Bioinformatics, 17(Suppl 1):S243-52, 2001.
13. D. Spiegelhalter, A. Thomas, N. Best, and W. Gilks. BUGS: Bayesian inference using Gibbs sampling. MRC Bio statistics Unit, Cambridge, England, www.mrc-bsu.cam.ac.uk/bugs, 1994.
14. R. M. Stallman and G. J. Sussman. Forward reasoning and dependency directed backtracking in a system for computer-aided circuit analysis. AI Memo 380, MIT Artificial Intelligence Laboratory, September 1976.
15. S. Thrun, M. Montemerlo, and A. Aron. Probabilistic Terrain Analysis For High-Speed Desert Driving. Proc. Robotics Science and Systems, Philadelphia, PA, USA, August, pages 16-19, 2006.