Results 11 to 20 of about 289,279 (277)
Bayesian Distributionally Robust Optimization
SIAM Journal on Optimization, 2023 We introduce a new framework, Bayesian Distributionally Robust Optimization (Bayesian-DRO), for data-driven stochastic optimization where the underlying distribution is unknown. Bayesian-DRO contrasts with most of the existing DRO approaches in the use of Bayesian estimation of the unknown distribution.
Alexander Shapiro, Enlu Zhou, Yifan Lin
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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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Warm starting Bayesian optimization [PDF]
2016 Winter Simulation Conference (WSC), 2016 To Appear in the Proc.
Poloczek, Matthias +2 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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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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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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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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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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Bayesian Gait Optimization for Bipedal Locomotion [PDF]
, 2014 One of the key challenges in robotic bipedal locomotion is finding gait parameters that optimize a desired performance criterion, such as speed, robustness or energy efficiency.
Calandra, R +4 more
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