Uncertainty in Artificial Intelligence
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Monolingual Probabilistic Programming Using Generalized Coroutines
Oleg Kiselyov, Chung-chieh Shan
Probabilistic programming languages and modeling toolkits are two modular ways to build and reuse stochastic models and inference procedures. Combining strengths of both, we express models and inference as generalized coroutines in the same general-purpose language. We use existing facilities of the language, such as rich libraries, optimizing compilers, and types, to develop concise, declarative, and realistic models with competitive performance on exact and approximate inference. In particular, a wide range of models can be expressed using memoization. Because deterministic parts of models run at full speed, custom inference procedures are trivial to incorporate, and inference procedures can reason about themselves without interpretive overhead. Within this framework, we introduce a new, general algorithm for importance sampling with look-ahead.
Keywords: null
Pages: 285-292
PS Link:
PDF Link: /papers/09/p285-kiselyov.pdf
AUTHOR = "Oleg Kiselyov and Chung-chieh Shan",
TITLE = "Monolingual Probabilistic Programming Using Generalized Coroutines",
BOOKTITLE = "Proceedings of the Twenty-Fifth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-09)",
ADDRESS = "Corvallis, Oregon",
YEAR = "2009",
PAGES = "285--292"

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