Results 11 to 20 of about 11,061,498 (228)
Gaussian process model based predictive control [PDF]
, 2004 Gaussian process models provide a probabilistic non-parametric modelling approach for black-box identification of non-linear dynamic systems. The Gaussian processes can highlight areas of the input space where prediction quality is poor, due to the lack ...
Rasmussen, C.E. +3 more
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Variational Bayesian multinomial probit regression with Gaussian process priors [PDF]
, 2006 It is well known in the statistics literature that augmenting binary and polychotomous response models with Gaussian latent variables enables exact Bayesian analysis via Gibbs sampling from the parameter posterior.
Rogers, S., Girolami, M.
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Warped Gaussian process modelling of transcriptional regulation [PDF]
, 2012 This article extends recent work on Gaussian process modelling of transcriptional regulation, which assumed additive Gaussian noise of constant variance, to heteroscedastic noise.
Husmeier, D., Ji, R.
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Derivative observations in Gaussian Process models of dynamic systems [PDF]
, 2003 Gaussian processes provide an approach to nonparametric modelling which allows a straightforward combination of function and derivative observations in an empirical model.
Rasmussen, C.E. +4 more
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Adaptive, cautious, predictive control with Gaussian process priors [PDF]
, 2003 Nonparametric Gaussian Process models, a Bayesian statistics approach, are used to implement a nonlinear adaptive control law. Predictions, including propagation of the state uncertainty are made over a k-step horizon.
Rasmussen, C.E. +3 more
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Multisensor Estimation Fusion with Gaussian Process for Nonlinear Dynamic Systems
Entropy, 2019 The Gaussian process is gaining increasing importance in different areas such as signal processing, machine learning, robotics, control and aerospace and electronic systems, since it can represent unknown system functions by posterior probability.
Yiwei Liao +3 more
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Journal of Asian Architecture and Building Engineering, 2021 With building energy codes getting strict, quantitative analysis is necessary in the early design stage of high-energy-performance buildings. To fully explore the design space, a highly efficient method is necessary.
Yun Gao +2 more
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An automation system for vehicle driveability evaluation using machine learning
Nihon Kikai Gakkai ronbunshu, 2022 The drivability is one of the important aspects of vehicle dynamic performances. To ensure quality of the drivability performance, comprehensive screening evaluation is necessary by controlling both complicated driver operation and vehicle behavior ...
Hisashi TAJIMA +4 more
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Global Optimization Employing Gaussian Process-Based Bayesian Surrogates
Entropy, 2018 The simulation of complex physics models may lead to enormous computer running times. Since the simulations are expensive it is necessary to exploit the computational budget in the best possible manner.
Roland Preuss, Udo von Toussaint
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Applied Sciences, 2019 Precise prediction of short-term electric load demand is the key for developing power market strategies. Due to the dynamic environment of short-term load forecasting, probabilistic forecasting has become the center of attention for its ability of ...
Zhengmin Kong +3 more
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