Results 31 to 40 of about 4,909,083 (351)
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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Nihon Kikai Gakkai ronbunshu, 2022 Monitoring structural integrity has been demanded to achieve a sustainable society against disasters, including seismic and extreme wind events, and thus Structural Health Monitoring (SHM) system is one of the significant technologies.
Tsuyoshi FUKASAWA +2 more
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Applied Sciences, 2021 Land cover classification is able to reflect the potential natural and social process in urban development, providing vital information to stakeholders. Recent solutions on land cover classification are generally addressed by remotely sensed imagery and ...
Tianxiang Zhang +4 more
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Warm starting Bayesian optimization [PDF]
2016 Winter Simulation Conference (WSC), 2016 To Appear in the Proc.
Poloczek, Matthias +2 more
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Surrogate modeling of waveform response using singular value decomposition and Bayesian optimization
Journal of Advanced Mechanical Design, Systems, and Manufacturing, 2021 In the early stage of vehicle development, it is required to implement a target cascading study by solving inverse problems. However, simulation costs of vehicle dynamics to predict transient responses and frequency responses make the target cascading ...
Kohei SHINTANI +4 more
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Scalarizing Functions in Bayesian Multiobjective Optimization [PDF]
, 2019 Scalarizing functions have been widely used to convert a multiobjective optimization problem into a single objective optimization problem. However, their use in solving (computationally) expensive multi- and many-objective optimization problems in ...
Chugh, Tinkle
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Real-time load forecasting model for the smart grid using bayesian optimized CNN-BiLSTM
Frontiers in Energy Research, 2023 A smart grid is a new type of power system based on modern information technology, which utilises advanced communication, computing and control technologies and employs advanced sensors, measurement, communication and control devices that can monitor the
Daohua Zhang +4 more
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Application of Bayesian optimized XGBoost in seismic interpretation of small-scale faults
Meitan xuebao, 2023 In order to further improve the identification accuracy of small-scale faults in seismic interpretation, Bayesian optimized extreme gradient boosting (XGBoost) model was constructed to recognize small-scale faults across coalbeds using reduced seismic ...
Changwei DING +3 more
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BayesO: A Bayesian optimization framework in Python
Journal of Open Source Software, 2023 Bayesian optimization is a sample-efficient method for solving the optimization of a black-box function. In particular, it successfully shows its effectiveness in diverse applications such as hyperparameter optimization, automated machine learning ...
Jungtaek Kim, Seungjin Choi
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Bayesian Optimization Based on K-Optimality [PDF]
Entropy, 2018 Bayesian optimization (BO) based on the Gaussian process (GP) surrogate model has attracted extensive attention in the field of optimization and design of experiments (DoE). It usually faces two problems: the unstable GP prediction due to the ill-conditioned Gram matrix of the kernel and the difficulty of determining the trade-off parameter between ...
Liang Yan +3 more
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