Fusion, propagation, and structuring in belief networks
Artificial Intelligence
Operations Research
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Enhancement schemes for constraint processing: backjumping, learning, and cutset decomposition
Artificial Intelligence
Probabilistic inference in multiply connected belief networks using loop cutsets
International Journal of Approximate Reasoning
A note on approximation of the vertex cover and feedback vertex set problems—unified approach
Information Processing Letters
SODA '94 Proceedings of the fifth annual ACM-SIAM symposium on Discrete algorithms
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Constant Ratio Approximations of the Weighted Feedback Vertex Set Problem for Undirected Graphs
ISAAC '95 Proceedings of the 6th International Symposium on Algorithms and Computation
Causal networks: semantics and expressiveness
UAI '88 Proceedings of the Fourth Annual Conference on Uncertainty in Artificial Intelligence
UAI '88 Proceedings of the Fourth Annual Conference on Uncertainty in Artificial Intelligence
Ideal reformulation of belief networks
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
A computational model for causal and diagnostic reasoning in inference systems
IJCAI'83 Proceedings of the Eighth international joint conference on Artificial intelligence - Volume 1
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
An empirical study of w-cutset sampling for bayesian networks
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Slightly superexponential parameterized problems
Proceedings of the twenty-second annual ACM-SIAM symposium on Discrete Algorithms
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We show how to find a minimum loop cutset in a Bayesian network with high probability. Finding such a loop cutset is the first step in Pearl's method of conditioning for inference. Our random algorithm for finding a loop cutset, called REPEATEDWGUESSI, out - puts a minimum loop cutset, after O(c.6kkn) steps, with probability at least 1 - (1 - 1/6k)c6k, where c 1 is a constant specified by the user, k is the size of a minimum weight loop cutset, and n is the number of vertices. We also show empirically that a variant of this algorithm, called WRA, often finds a loop cutset that is closer to the minimum loop cutset than the ones found by the best deterministic algorithms known.