Results 41 to 50 of about 300,432 (267)
Gaussian Processes on Hypergraphs
CoRR, 2021 25 pages, 6 ...
Thomas Pinder +3 more
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Gaussian Process Time-Series Models for Structures under Operational Variability
Frontiers in Built Environment, 2017 A wide range of vibrating structures are characterized by variable structural dynamics resulting from changes in environmental and operational conditions, posing challenges in their identification and associated condition assessment. To tackle this issue,
Luis David Avendaño-Valencia +3 more
doaj +1 more source
Power Load Forecasting Method Based on MT-BSGP
Journal of Harbin University of Science and Technology, 2021 In order to forecast short-term household power load，a power load forecasting method based on multi-task Bayesian spatiotemporal Gaussian process ( MT-BSGP) is proposed.
LI Zhi-yong +5 more
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Sequentially Estimating the Approximate Conditional Mean Using Extreme Learning Machines
Entropy, 2020 This study examined the extreme learning machine (ELM) applied to the Wald test statistic for the model specification of the conditional mean, which we call the WELM testing procedure.
Lijuan Huo, Jin Seo Cho
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Demand response of residential air conditioning load based on user behavior
Zhejiang dianli, 2023 Residential side demand response is an important supplementary means to maintain the supply-demand balance of source-load in the power system. However， the uncertainty of user behavior makes it difficult to accurately control demand response.
LIU Yiping +5 more
doaj +1 more source
Physical Review D, 2012 Gaussian processes provide a method for extracting cosmological information from observations without assuming a cosmological model. We carry out cosmography -- mapping the time evolution of the cosmic expansion -- in a model-independent manner using kinematic variables and a geometric probe of cosmology.
Shafieloo, Arman +2 more
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Recent developments in empirical dynamic modelling
Methods in Ecology and Evolution, 2023 Ecosystems are complex and sparsely observed making inference and prediction challenging. Empirical dynamic modelling (EDM) circumvents the need for a parametric model and complete observations of all system variables.
Stephan B. Munch +2 more
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Sparse On-Line Gaussian Processes [PDF]
Neural Computation, 2002 We develop an approach for sparse representations of gaussian process (GP) models (which are Bayesian types of kernel machines) in order to overcome their limitations for large data sets. The method is based on a combination of a Bayesian on-line algorithm, together with a sequential construction of a relevant subsample of the data that fully ...
Csato, Lehel, Opper, Manfred
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Conformations of Steroid Hormones: Infrared and Vibrational Circular Dichroism Spectroscopy
Molecules, 2023 Steroid hormone molecules may exhibit very different functionalities based on the associated functional groups and their 3D arrangements in space, i.e., absolute configurations and conformations.
Yanqing Yang +6 more
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Multiphase flow applications of nonintrusive reduced-order models with Gaussian process emulation
Data-Centric Engineering, 2022 Reduced-order models (ROMs) are computationally inexpensive simplifications of high-fidelity complex ones. Such models can be found in computational fluid dynamics where they can be used to predict the characteristics of multiphase flows.
Themistoklis Botsas +3 more
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