
Updating Probabilities in MultiplyConnected Belief Networks
This paper focuses on probability updates in multiplyconnected belief n...
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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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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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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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Properties of Bayesian Belief Network Learning Algorithms
Bayesian belief network learning algorithms have three basic components:...
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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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Distributed Revision of Belief Commitment in MultiHypothesis Interpretations
This paper extends the applications of beliefnetworks to include the re...
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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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