Gaussian process regression (GPR) is a non-parametric approach that can be used to make predictions based on a set of known points. It has been widely employed in recent years on a variety of problems. However the Gaussian process regression algorithm performs matrices inversions and the ...
Utilising this adaptive strategy, the Gaussian process based stochastic model predictive control (GP㏒MPC) algorithm is designed by applying the adaptive tightened constraints in all prediction horizons. To reduce the computation load, the one﹕tep GP㏒MPC algorithm is further developed by imposing the...
6. Very recently, further progress in efficiency has been achieved by developing an imaging-type multichannel spin detector. The effective figure-of-merit increases with the
A new class of parameter estimation algorithms is introduced for Gaussian process regression (GPR) models. It is shown that the integration of the GPR model with probability distance measures of (i) the integrated square error and (ii) Kullback–Leibler (K–L) divergence are analytically ...
Keywords:Gaussianprocess;hyper-parametersoptimization;memeticalgorithm;regressionmodel lIntr0ducti0n Beinganewkernelmethoddevelopedonthebasis ofstatisticallearningandBayesiantheorem.Gaussian process(GP)[1]iswelladaptabletoprocessingsuch complexproblemsasnon—linearity,smal1samplesize. ...
The proposed method integrates an appropriate kernel, benchmarked against the radiation transport code MCNP6, into the Gaussian Process Regression technique. The suitability of the proposed technique is demonstrated through its application to data collected from a bespoke robotic system used to conduct a...
Step-wise additive GP regression algorithm The space of all models is large and thus an exhaustive search for the best model over the whole model space would be too slow in practice. Two commonly used model (or feature) selection methods include forward and backward search techniques. Starting ...
Gaussian process regression(GPR) is an even,ner approach than this. Rather than claiming relates to some speci,c models (e.g. ),a Gaussianprocess can represent obliquely,but rigorously,by letting the data‘speak’moreclearly for themselves.GPR is still a form of supervised learning,but the tr...
Heteroscedastic Gaussian process regression This paper presents an algorithm to estimate simultaneously both mean and variance of a non parametric regression problem. The key point is that we are able to estimate vari-ance locally unlike standard Gaussian Pro-cess regression or SV... Quoc V. Le,Al...
Probabilistic surrogate modeling by Gaussian process: A new estimation algorithm for more robust prediction KeywordsComputer experimentsGaussian process regressionMachine learningOptimizationUncertaintyValidation criteriaNew algorithm for obtaining robust estimation of the ... A Marrel,B Iooss - 《Reliability Eng...