Results 11 to 20 of about 5,652,238 (291)
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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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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Nonlinear Multiscale Modelling and Design using Gaussian Processes [PDF]
Journal of Applied and Computational Mechanics, 2021 A method for nonlinear material modeling and design using statistical learning is proposed to assist in the mechanical analysis of structural materials. Conventional computational homogenization schemes are proven to underperform in analyzing the complex
Sumudu Herath, Udith Haputhanthri
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Detecting periodicities with Gaussian processes [PDF]
PeerJ Computer Science, 2016 We consider the problem of detecting and quantifying the periodic component of a function given noise-corrupted observations of a limited number of input/output tuples.
Nicolas Durrande +3 more
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Bayesian eikonal tomography using Gaussian processes
Seismica, 2023 Eikonal tomography has become a popular methodology for deriving phase velocity maps from surface wave phase delay measurements. Its high efficiency makes it popular for handling datasets deriving from large-N arrays, in particular in the ambient-noise
Jack Muir
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Classification of cassava leaf diseases using deep Gaussian transfer learning model
Engineering Reports, 2023 In Sub‐Saharan Africa, experts visually examine the plants and look for disease symptoms on the leaves to diagnose cassava diseases, a subjective method. Machine learning algorithms have been employed to quickly identify and classify crop diseases.
Ahishakiye Emmanuel +4 more
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Stokesian processes : inferring Stokes flows using physics-informed Gaussian processes
Machine Learning: Science and Technology, 2023 We develop a probabilistic Stokes flow framework, using physics informed Gaussian processes, which can be used to solve both forward/inverse flow problems with missing and/or noisy data.
John J Molina +2 more
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Oscillating Gaussian processes [PDF]
Statistical Inference for Stochastic Processes, 2020 AbstractIn this article we introduce and study oscillating Gaussian processes defined by$$X_t = \alpha _+ Y_t \mathbf{1}_{Y_t >0} + \alpha _- Y_t\mathbf{1}_{Y_t<0}$$Xt=α+Yt1Yt>0+α-Yt1Yt<0, where$$\alpha _+,\alpha _->0$$α+,α->0are free parameters andYis either stationary or self-similar Gaussian process.
Ilmonen, Pauliina +2 more
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Enhanced Checkerboard Detection Using Gaussian Processes
Mathematics, 2023 Accurate checkerboard detection is of vital importance for computer vision applications, and a variety of checkerboard detectors have been developed in the past decades.
Michaël Hillen +8 more
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