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**Likelihood Computations Using Value Abstraction**

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N. Friedman, D. Geiger, and N. Lotner.

To appear in *Proc. Sixteenth Conf. on Uncertainty in Artificial Intelligence
(UAI)*, 2000.
Postscript version (332K)

PDF version.

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Abstract

In this paper, we use evidence-specific value abstraction for speeding
Bayesian networks inference. This is done by grouping variable values and
treating the combined values as a single entity. As we show, such abstractions
can exploit regularities in conditional probability distributions and also
the specific values of observed variables. To formally justify value abstraction,
we define the notion of safe value abstraction and devise inference algorithms
that use it to reduce the cost of inference. Our procedure is particularly
useful for learning complex networks with many hidden variables. In such
cases, repeated likelihood computations are required for EM or other parameter
optimization techniques. Since these computations are repeated with respect
to the same evidence set, our methods can provide significant speedup to
the learning procedure. We demonstrate the algorithm on genetic linkage
problems where the use of value abstraction sometimes differentiates
between a feasible and non-feasible solution.

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