CTPPL: A continuous time probabilistic programming language

IJCAI International Joint Conference on Artificial Intelligence

Volumn , Issue , 2009, Pages 1943-1950

  Pfeffer, Avi ^a  

^a HARVARD UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ADA (PROGRAMMING LANGUAGE); CONTINUOUS TIME SYSTEMS; OBJECT ORIENTED PROGRAMMING; SEMANTICS;

A-PARTICLES; COMPLEX DATA STRUCTURES; CONTINUOUS-TIME; DYNAMIC PROCESS; PROBABILISTIC MODELS; PROBABILISTIC PROGRAMMING LANGUAGE; PROBABILITY MEASURES; SHOW THROUGH;

MODELING LANGUAGES;

EID: 78751685120     PISSN: 10450823     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (11)

1. A. T. Cemgil and B. Kappen. Monte Carlo method for tempo tracking and rhythm quantization. Journal of Artificial Intelligence Research, 18:45-81, 2003.
2. R. de Salvo Braz, N. Arora, E. Sudderth, and S. Russell. Open-universe state estimation with D-BLOG. Poster presented at NIPS 2008 workshop on probabilistic programming, 2008.
3. Y. Fan and C. R. Shelton. Sampling for approximate inference in continuous time bayesian networks. In International Symposium on Artificial Intelligence and Mathematics, 2008.
4. N. D. Goodman, V. K. Mansinghka, D. Roy, K. Bonawitz, and J. B. Tenenbaum. Church: A language for generative models. In Uncertainty in Artificial Intelligence, 2008.
5. K. Gopalratnam, H. Kautz, and D. S. Weld. Extending continuous time Bayesian networks. In National Conference on Artificial Intelligence (AAAI), 2005.
6. B. Milch. Probabilistic Models with Unknown Objects. PhD thesis, Computer Science Division, University of California, Berkeley, 2006.
7. B. Ng, A. Pfeffer, and R. Dearden. Continuous time particle filtering. In International Joint Conference on Artificial Intelligence, 2005.
8. U. Nodelman, C. R. Shelton, and D. Koller. Expectation maximization and complex duration distributions for continuous time Bayesian networks. In Uncertainty in Artificial Intelligence, 2005.
9. U. Nodelman. Continuous Time Bayesian Networks. PhD thesis, Stanford University, 2007.
10. A. Pfeffer. Functional specification of probabilistic process models. In National Conference on Artificial Intelligence (AAAI), 2005.
11. A. Pfeffer. The design and implementation of IBAL: A general-purpose probabilistic language. In Statistical Relational Learning. MIT Press, 2007.