Uncertainty in Artificial Intelligence
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Context-Specific Approximation in Probabilistic Inference
David Poole
Abstract:
There is evidence that the numbers in probabilistic inference don't really matter. This paper considers the idea that we can make a probabilistic model simpler by making fewer distinctions. Unfortunately, the level of a Bayesian network seems too coarse; it is unlikely that a parent will make little difference for all values of the other parents. In this paper we consider an approximation scheme where distinctions can be ignored in some contexts, but not in other contexts. We elaborate on a notion of a parent context that allows a structured context-specific decomposition of a probability distribution and the associated probabilistic inference scheme called probabilistic partial evaluation (Poole 1997). This paper shows a way to simplify a probabilistic model by ignoring distinctions which have similar probabilities, a method to exploit the simpler model, a bound on the resulting errors, and some preliminary empirical results on simple networks.
Keywords:
Pages: 447-454
PS Link: ftp://ftp.cs.ubc.ca/ftp/local/poole/papers/approx.ps.gz
PDF Link: /papers/98/p447-poole.pdf
BibTex:
@INPROCEEDINGS{Poole98,
AUTHOR = "David Poole ",
TITLE = "Context-Specific Approximation in Probabilistic Inference",
BOOKTITLE = "Proceedings of the Fourteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-98)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1998",
PAGES = "447--454"
}


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