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]], pp523-560 (and here). www.doi.org: 10.1093/comjnl/bxm117 D. L. Dowe (2011a), "MML, hybrid Bayesian network graphical models, statistical consistency, invariance and uniqueness", Handbook of the
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]], pp523-560 (and here). www.doi.org: 10.1093/comjnl/bxm117 D. L. Dowe (2011a), "MML, hybrid Bayesian network graphical models, statistical consistency, invariance and uniqueness", Handbook of the
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intelligence expertise from the Faculty of Information Technology with gravitational-wave astrophysics expertise from the School of Physics and Astronomy in the Faculty of Science. We will apply Bayesian
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intelligence expertise from the Faculty of Information Technology with gravitational-wave astrophysics expertise from the School of Physics and Astronomy in the Faculty of Science. We will apply Bayesian
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sequencing (Oxford Nanopore), data mining of electronic medical records and use of machine learning to predict several outcomes. Among the approaches used will be the Bayesian information-theoretic
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sequencing (Oxford Nanopore), data mining of electronic medical records and use of machine learning to predict several outcomes. Among the approaches used will be the Bayesian information-theoretic
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: Robust uncertainty estimation: Improve calibration and uncertainty reliability under drift (e.g., ensembles, Bayesian approximations, conformal prediction). O3: Joint OOD detection + AL: Combine
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: Robust uncertainty estimation: Improve calibration and uncertainty reliability under drift (e.g., ensembles, Bayesian approximations, conformal prediction). O3: Joint OOD detection + AL: Combine
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integrate approaches from decision theory, Bayesian experimental design, Partially Observable Markov decision processes, reinforcement learning and ecological modelling to support sequential decision
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integrate approaches from decision theory, Bayesian experimental design, Partially Observable Markov decision processes, reinforcement learning and ecological modelling to support sequential decision