Conditioning Methods for Exact and Approximate Inference in Causal Networks
Artificial Intelligence
2013-02-21 v1
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.
Cite
@article{arxiv.1302.4939,
title = {Conditioning Methods for Exact and Approximate Inference in Causal Networks},
author = {Adnan Darwiche},
journal= {arXiv preprint arXiv:1302.4939},
year = {2013}
}
Comments
Appears in Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence (UAI1995)