Exploiting Case-Based Independence for Approximating Marginal Probabilities
Document Type
Article
Publication Date
1-1996
Abstract
Computing marginal probabilities in Bayes networks is a hard problem. Deterministic anytime approximation schemes accumulate the probability mass in a small number of value assignments to the network variables. Under certain assumptions, the probability mass in the assignments is sufficient to obtain a good approximation. Such methods are especially useful for highly connected networks, where the topology makes the exact algorithms intractable. Bayes networks often possess a fine independence structure not evident from the topology, but apparent in local conditional distributions. Independence-based (IB) assignments, originally proposed as a theory of abduction, take advantage of such independence, and thus contain fewer assigned variables-and more probability mass. We present several algorithms that use IB assignments for approximating marginal probabilities. Experimental results suggest that this approach is feasible for highly connected belief networks. Abstract © Elsevier
DOI
10.1016/0888-613X(95)00112-T
Source Publication
International Journal of Approximate Reasoning
Recommended Citation
Shimony, S. E., & Santos, E. (1996). Exploiting case-based independence for approximating marginal probabilities. International Journal of Approximate Reasoning, 14(1), 25–54. https://doi.org/10.1016/0888-613X(95)00112-T
Comments
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