
Random Algorithms for the Loop Cutset Problem
We show how to find a minimum loop cutset in a Bayesian network with hig...
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De(con)struction of the lazyF loop: improving performance of Smith Waterman alignment
Striped variation of the SmithWaterman algorithm is known as extremely ...
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Randomized Algorithms for the Loop Cutset Problem
We show how to find a minimum weight loop cutset in a Bayesian network w...
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On Heuristics for Finding Loop Cutsets in MultiplyConnected Belief Networks
We introduce a new heuristic algorithm for the problem of finding minimu...
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Updating Probabilities in MultiplyConnected Belief Networks
This paper focuses on probability updates in multiplyconnected belief n...
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Loop Summarization with Rational Vector Addition Systems (extended version)
This paper presents a technique for computing numerical loop summaries. ...
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Closedloop field development optimization with multipoint geostatistics and statistical assessment
Closedloop field development (CLFD) optimization is a comprehensive fra...
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Approximation Algorithms for the Loop Cutset Problem
We show how to find a small loop curser in a Bayesian network. Finding such a loop cutset is the first step in the method of conditioning for inference. Our algorithm for finding a loop cutset, called MGA, finds a loop cutset which is guaranteed in the worst case to contain less than twice the number of variables contained in a minimum loop cutset. We test MGA on randomly generated graphs and find that the average ratio between the number of instances associated with the algorithms' output and the number of instances associated with a minimum solution is 1.22.
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