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
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Artificial Intelligence - special issue on computational tradeoffs under bounded resources
Optimizing exact genetic linkage computations
RECOMB '03 Proceedings of the seventh annual international conference on Research in computational molecular biology
Evidence Absorption and Propagation through Evidence Reversals
UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Computational advantages of relevance reasoning in Bayesian belief networks
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Context-specific independence in Bayesian networks
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Bucket elimination: a unifying framework for probabilistic inference
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
New advances in inference by recursive conditioning
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Structure identification of Bayesian classifiers based on GMDH
Knowledge-Based Systems
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Genetic linkage analysis is a statistical method for mapping genes onto chromosomes, and is useful for detecting and predicting diseases. One of its current limitations is the computational complexity of the problems of interest. This research presents methods for mapping genetic linkage problems as Bayesian networks and then addresses novel techniques for making the problems more tractable. The result is a new tool for solving these problems called RC_Link, which in many cases is orders of magnitude faster than existing tools.