Gaussian process regression
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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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
... 2.1. Gaussian process regression We consider Gaussian process regression, where we observe training data, D = {x i , y i } N i=1 with x i ∈ X and y i ∈ ...
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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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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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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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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
... the Gaussian process regression problem for large ...a regression domain into multiple local regions and then assumes a local GP model for each local ...auxiliary process that ...
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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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Multi-fidelity Gaussian process regression for prediction of random fields
... [25] ) to each component of the vector of Fourier coefficients. From a Bayesian standpoint, this is equivalent to assuming independent priors for each model output, which may result in loss of information. To overcome this ...
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Single Image Super-Resolution using Gaussian Process Regression
... {07821020d, [email protected]} Abstract In this paper we address the problem of producing a high-resolution image from a single low-resolution image without any external training set. We propose a framework for both ...
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GPLP: A Local and Parallel Computation Toolbox for Gaussian Process Regression
... localized regression methods: the domain decomposition method (Park et ...probabilistic regression (Urtasun and Darrell, 2008, LPR), and bagging for Gaussian process regression (Chen ...
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Invariances of random fields paths, with applications in Gaussian Process Regression
... the Gaussian Process Regression terminology, see, ...a Gaussian random field model is assumed for the function of interest, and so all prior assumptions on this function are incorporated ...
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Determination of Strain Energy for Triggering Liquefaction Based on Gaussian Process Regression
... Centre for Disaster Mitigation and Management, VIT University, Vellore 632014, India. *Email: [email protected] Abstract. The determination of seismic liquefaction potential of soil is an imperative task in earthquake ...
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A spatio temporal, Gaussian Process Regression, real estate price predictor
... ABSTRACT This paper introduces a novel four-stage methodol- ogy for real-estate valuation. This research shows that space, property, economic, neighbourhood and time features are all contributing factors in produc- ing a ...
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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
... A naive approach would be to simply read off the age range’s mean. A slightly more advanced method could interpolate between bin centres. The former method fails to use the data in the neighbouring bins to assist with ...
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A Unifying View of Sparse Approximate Gaussian Process Regression
... Regression models based on Gaussian processes (GPs) are simple to implement, flexible, fully probabilistic models, and thus a powerful tool in many areas of application. Their main limitation is that memory ...
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