Results 21 to 30 of about 2,251,103 (302)
Advances in Neural Information Processing Systems 36, 2023 Accepted to NeurIPS ...
Zhongxiang Dai +5 more
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Efficient utility-based clustering over high dimensional partition spaces [PDF]
, 2009 Because of the huge number of partitions of even a moderately sized dataset, even when Bayes factors have a closed form, in model-based clustering a comprehensive search for the highest scoring (MAP) partition is usually impossible.
Smith, JQ +9 more
core +1 more source
Bayesian quantification of thermodynamic uncertainties in dense gas flows [PDF]
, 2014 A Bayesian inference methodology is developed for calibrating complex equations of state used in numerical fluid flow solvers. Precisely, the input parameters of three equations of state commonly used for modeling the thermodynamic behavior of so-called ...
CINNELLA, Paola, X. Merle, MERLE, Xavier
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Bayesian optimization of nanoporous materials [PDF]
, 2021 Nanoporous materials (NPMs) could be used to store, capture, and sense many different gases. Given an adsorption task, we often wish to search a library of NPMs for the one with the optimal adsorption property.
Aryan, Deshwal +2 more
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Evolution-guided Bayesian optimization for constrained multi-objective optimization in self-driving labs [PDF]
, 2023 The development of automated high-throughput experimental platforms has enabled fast sampling of high-dimensional decision spaces. To reach target properties efficiently, these platforms are increasingly paired with intelligent experimental design.
Saif A, Khan +10 more
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Replication Data for: eSLP optimization algorithm
, 2023 m-files implementing the eSLP optimization algorithm for the three simulations (3.1. - 3.3.) described in the associated paper. File README.txt contains analytical directions regarding requirements, how to verify the results and videos generated by the ...
Optimization, eSLP
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Bayesian Optimization with Gradients
CoRR, 2017 Bayesian optimization has been successful at global optimization of expensive-to-evaluate multimodal objective functions. However, unlike most optimization methods, Bayesian optimization typically does not use derivative information. In this paper we show how Bayesian optimization can exploit derivative information to decrease the number of objective ...
Jian Wu +3 more
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Prognostic Modelling with Dynamic Bayesian Networks [PDF]
, 2009 In this paper, we review the application of dynamic Bayesian networks to prognostic modelling. An example is provided for illustration. With this example, we show how the equipment’s reliability decays over time in the situation where repair is not ...
McNaught, Ken R., Zagorecki, A.
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Optimal Bayesian Randomization
Journal of the Royal Statistical Society Series B: Statistical Methodology, 1997 Summary Randomization is a puzzle for Bayesians. The intuitive need for randomization is clear, but there is a standard result that Bayesians need not randomize. In this paper we propose a model in which randomization is a strictly optimal procedure.
Berry, Scott M., Kadane, Joseph B.
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Application of Improved LightGBM Model in Blood Glucose Prediction
Applied Sciences, 2020 In recent years, with increasing social pressure and irregular schedules, many people have developed unhealthy eating habits, which has resulted in an increasing number of patients with diabetes, a disease that cannot be cured under the current medical ...
Yan Wang, Tao Wang
doaj +1 more source