
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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Approximation Algorithms for the Loop Cutset Problem
We show how to find a small loop curser in a Bayesian network. Finding s...
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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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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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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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Sense Amplifier Comparator with Offset Correction for Decision Feedback Equalization based Receivers
A decision feedback circuit with integrated offset compensation is prese...
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Random Algorithms for the Loop Cutset Problem
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 "Repeated WGuessI", outputs a minimum loop cutset, after O(c 6^k k n) steps, with probability at least 1(1 over6^k)^c 6^k), 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.
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