Efficient Search-Based Inference for Noisy-OR Belief Networks: TopEpsilon

Kurt Huang; Max Henrion

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arXiv · 1302.3584

Efficient Search-Based Inference for Noisy-OR Belief Networks: TopEpsilon

Kurt Huang·Max Henrion

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Abstract

Inference algorithms for arbitrary belief networks are impractical for large, complex belief networks. Inference algorithms for specialized classes of belief networks have been shown to be more efficient. In this paper, we present a search-based algorithm for approximate inference on arbitrary, noisy-OR belief networks, generalizing earlier work on search-based inference for two-level, noisy-OR belief networks. Initial experimental results appear promising.

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Kurt Huang, Max Henrion. 2013-02-13. Efficient Search-Based Inference for Noisy-OR Belief Networks: TopEpsilon. https://arxiv.org/abs/1302.3584

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