Uncertainty in Artificial Intelligence
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Gibbs Sampling in Open-Universe Stochastic Languages
Nimar Arora, Rodrigo de Salvo Braz, Erik Sudderth, Stuart Russell
Languages for open-universe probabilistic models (OUPMs) can represent situations with an unknown number of objects and iden- tity uncertainty. While such cases arise in a wide range of important real-world appli- cations, existing general purpose inference methods for OUPMs are far less efficient than those available for more restricted lan- guages and model classes. This paper goes some way to remedying this deficit by in- troducing, and proving correct, a generaliza- tion of Gibbs sampling to partial worlds with possibly varying model structure. Our ap- proach draws on and extends previous generic OUPM inference methods, as well as aux- iliary variable samplers for nonparametric mixture models. It has been implemented for BLOG, a well-known OUPM language. Combined with compile-time optimizations, the resulting algorithm yields very substan- tial speedups over existing methods on sev- eral test cases, and substantially improves the practicality of OUPM languages generally.
Pages: 30-39
PS Link:
PDF Link: /papers/10/p30-arora.pdf
AUTHOR = "Nimar Arora and Rodrigo de Salvo Braz and Erik Sudderth and Stuart Russell",
TITLE = "Gibbs Sampling in Open-Universe Stochastic Languages",
BOOKTITLE = "Proceedings of the Twenty-Sixth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-10)",
ADDRESS = "Corvallis, Oregon",
YEAR = "2010",
PAGES = "30--39"

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