Gaussian process for regression
Sparse Spectrum Gaussian Process Regression
... Spectrum Gaussian Process (SSGP) algorithm, a novel perspective on sparse GP approximations where rather than the usual sparsity approximation in the spatial do- main, it is the spectrum of the covariance ...
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Gaussian process regression for binned data
... performing Gaussian Process (GP) regression given such binned ...data. Gaussian Processes are a principled probabilistic method for performing ...
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Compressed Gaussian Process for Manifold Regression
... the regression function in the coordinates on this subspace, providing a characterization of predictive ...logistic Gaussian process approach, while Reich et ...from Gaussian process ...
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Automatic Gait Optimization with Gaussian Process Regression
... the process ex- ist, most involve local function optimization pro- cedures that suffer from three key ...on Gaussian process regression that addresses all three ...
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The Gaussian Process Autoregressive Regression Model (GPAR)
... Multi-output regression models must exploit dependencies between outputs to maximise predictive ...of Gaussian processes (GPs) to this setting typi- cally yields models that are computationally demanding ...
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Application of Gaussian Process Priors on Bayesian Regression
... the regression parameters and the mean response probability (Czado and Santner, ...binary regression model, Li et al. (2016) used Gaussian process prior on the latent regression ...
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Gaussian Process Regression for Vessel Performance Monitoring
... how Gaussian Process Regression (GPR) can be used equally good or better than Artificial Neural Networks (ANN) for short and long term predictions of the energy consumption on a ...
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Rates of Convergence for Sparse Variational Gaussian Process Regression
... to Gaussian process posteriors have been developed which avoid the O N 3 scaling with dataset size N ...for regression with normally dis- tributed inputs in D-dimensions with the Squared ...
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Rainfall Prediction using Gaussian Process Regression Classifier
... Forecasting provides information about the impending future measures and their consequences for the administration. Prediction of rainfall can play impotent role for WRM (Water resource management). After studying ...
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Gaussian process regression with functional covariates and multivariate response
... and Process Engineering, University of Surrey, Guildford GU2 7XH, UK c Sigma (Maths and Stats Support), Coventry University, Coventry CV1 5DD, UK Abstract Gaussian process regression (GPR) has ...
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Gaussian process functional regression modelling for batch data.
... stochastic process with zero mean and covari- ance kernel function C(x, x 0 ) with x = ...a Gaussian process regression (GPR) model for τ m (x) in ...a Gaussian process prior is ...
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Multi-fidelity Gaussian process regression for computer experiments
... Finally, we address in Chapter 8 the problem of global sensitivity analysis for stochastic simulators. As seen previously, variance-based sensitivity methods require a large number of simulations. As the computer codes ...
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Over-Fitting in Model Selection with Gaussian Process Regression
... in Gaussian Process Regression (GPR) seeks to determine the optimal values of the hyper-parameters governing the covariance function, which allows flexible customization of the GP to the problem at ...
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Robust Gaussian Process Regression with a Student-t Likelihood
... This paper considers the robust and efficient implementation of Gaussian process regression with a Student-t observation model, which has a non-log-concave likelihood. The challenge with the ...
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Patchwork Kriging for Large-scale Gaussian Process Regression
... for Gaussian process (GP) regression for large ...the regression input domain into multiple local regions with a different local GP model fitted in each ...for regression domains having ...
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A Gaussian Process Regression Model for the Traveling Salesman Problem
... method, Gaussian Process Regression (GPR) and the iterated local search is proposed to solve a deterministic symmetric TSP with a single ...a Gaussian process regression ...
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Ensemble Kalman filtering for online Gaussian process regression and learning
... the Gaussian process estimation provide satisfactory predic- tive accuracy using significantly less computational time in comparison to the GP regression without online ...
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Gaussian Process Regression for Prediction of Sulfate Content in Lakes of China
... of Gaussian process regression was used to build a prediction model for the sulphate content of lakes using several water quality variables as ...different Gaussian process ...
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Approximate inference in related multi-output Gaussian Process Regression
... Keywords: Gaussian Process, Kernel Methods, Approximate Inference, Multi-Output Regression, Flight-test data 1 Introduction The main difference between the physical sciences and machine learning can ...
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Scalable Hierarchical Gaussian Process Models for Regression and Pattern Classification
... sparse Gaussian processes for regression,” Machine Learning, ...2018. Gaussian process models have become the dominant approach to nonparamet- ric Bayesian regression, but their ...
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