Conditioning Methods for Exact and Approximate Inference in Causal Networks

Adnan Darwiche

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arXiv · 1302.4939

Conditioning Methods for Exact and Approximate Inference in Causal Networks

Adnan Darwiche

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Abstract

We present two algorithms for exact and approximate inference in causal networks. The first algorithm, dynamic conditioning, is a refinement of cutset conditioning that has linear complexity on some networks for which cutset conditioning is exponential. The second algorithm, B-conditioning, is an algorithm for approximate inference that allows one to trade-off the quality of approximations with the computation time. We also present some experimental results illustrating the properties of the proposed algorithms.

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Adnan Darwiche. 2013-02-20. Conditioning Methods for Exact and Approximate Inference in Causal Networks. https://arxiv.org/abs/1302.4939

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