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Most probable explanations in Bayesian networks: Complexity and tractability.

. Int. J. Approx. Reason., 52 (9): 1452-1469 (2011)

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Complexity results for enhanced qualitative probabilistic networks., and . Int. J. Approx. Reason., 48 (3): 879-888 (2008)Motivating explanations in Bayesian networks using MAP-independence.. Int. J. Approx. Reason., (2023)The Computational Complexity of Sensitivity Analysis and Parameter Tuning, and . CoRR, (2012)Benchmarking energy consumption and latency for neuromorphic computing in condensed matter and particle physics., , , , , , , , , and 3 other author(s). CoRR, (2022)The Parameterized Complexity of Approximate Inference in Bayesian Networks.. Probabilistic Graphical Models, volume 52 of JMLR Workshop and Conference Proceedings, page 264-274. JMLR.org, (2016)A Spiking Neuron Implementation of Genetic Algorithms for Optimization., , , , and . BNAIC/BENELEARN (Selected Papers), volume 1398 of Communications in Computer and Information Science, page 91-105. Springer, (2020)Predictive coding and the Bayesian brain: Intractability hurdles that are yet to be overcome., and . CogSci, cognitivesciencesociety.org, (2013)Most Inforbable Explanations: Finding Explanations in Bayesian Networks That Are Both Probable and Informative.. ECSQARU, volume 7958 of Lecture Notes in Computer Science, page 328-339. Springer, (2013)Bayesian Intractability Is Not an Ailment That Approximation Can Cure., , and . Cogn. Sci., 35 (5): 779-784 (2011)Modelling Uncertainty in Agent Programming., and . DALT, volume 3904 of Lecture Notes in Computer Science, page 17-32. Springer, (2005)