Effective Bayesian Inference for Stochastic Programs (1997)
Cached
Download Links
- [www.eecs.harvard.edu]
- [ai.stanford.edu]
- [ai.stanford.edu]
- [robotics.stanford.edu]
- DBLP
Other Repositories/Bibliography
| Venue: | In Proceedings of the 14th National Conference on Artificial Intelligence (AAAI |
| Citations: | 39 - 7 self |
BibTeX
@INPROCEEDINGS{Koller97effectivebayesian,
author = {Daphne Koller and David Mcallester and Avi Pfeffer},
title = {Effective Bayesian Inference for Stochastic Programs},
booktitle = {In Proceedings of the 14th National Conference on Artificial Intelligence (AAAI},
year = {1997},
pages = {740--747},
publisher = {AAAI Press}
}
Years of Citing Articles
Bookmark
 |
 |
 |
 |
 |
 |
OpenURL
Abstract
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) j 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 languageand the efficiency of the inference algorithm by encoding both Bay...
Citations
No citations identified.
