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The Complexity of Finding kth Most Probable Explanations in Probabilistic Networks.

, , and . SOFSEM, volume 6543 of Lecture Notes in Computer Science, page 356-367. Springer, (2011)

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Motivating explanations in Bayesian networks using MAP-independence.. Int. J. Approx. Reason., (2023)Complexity results for enhanced qualitative probabilistic networks., and . Int. J. Approx. Reason., 48 (3): 879-888 (2008)Benchmarking energy consumption and latency for neuromorphic computing in condensed matter and particle physics., , , , , , , , , and 3 other author(s). CoRR, (2022)The Computational Complexity of Sensitivity Analysis and Parameter Tuning, and . CoRR, (2012)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)A spiking neural algorithm for the Network Flow problem., and . CoRR, (2019)The mobile-paradigm as measure of infants' sense of agency? Insights from babybot simulations., , , , and . ICDL-EPIROB, page 41-42. IEEE, (2016)Treewidth and the Computational Complexity of MAP Approximations.. Probabilistic Graphical Models, volume 8754 of Lecture Notes in Computer Science, page 271-285. Springer, (2014)