
Updating Probabilities in MultiplyConnected Belief Networks
This paper focuses on probability updates in multiplyconnected belief n...
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An efficient approach for finding the MPE in belief networks
Given a belief network with evidence, the task of finding the I most pro...
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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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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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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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A New Algorithm for Finding MAP Assignments to Belief Networks
We present a new algorithm for finding maximum aposterior) (MAP) assign...
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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 minimum size loop cutsets in multiply connected belief networks. We compare this algorithm to that proposed in [Suemmondt and Cooper, 1988]. We provide lower bounds on the performance of these algorithms with respect to one another and with respect to optimal. We demonstrate that no heuristic algorithm for this problem cam be guaranteed to produce loop cutsets within a constant difference from optimal. We discuss experimental results based on randomly generated networks, and discuss future work and open questions.
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