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fast Gaussian process regression

Domain Decomposition Approach for Fast Gaussian Process Regression of Large Spatial Data Sets

Domain Decomposition Approach for Fast Gaussian Process Regression of Large Spatial Data Sets

... localized regression, we propose a new local kriging approach that explicitly addresses the problem of discontinuity in prediction on the boundaries of ...GP regression as an optimization problem and to ...

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Wind turbine rotor acceleration : identification using Gaussian regression

Wind turbine rotor acceleration : identification using Gaussian regression

... novel fast algorithms for the Gaussian process methodology that require less memory ...to Gaussian process regression may fail even with medium-scale datasets due to lack of ...

8

A Framework for Evaluating Approximation Methods for Gaussian Process Regression

A Framework for Evaluating Approximation Methods for Gaussian Process Regression

... provides fast MVMs for the data sets in our ...standardized regression problems, lengthscales of ≈1 (the width of the input distribution) are also ...

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Gaussian Kullback-Leibler Approximate Inference

Gaussian Kullback-Leibler Approximate Inference

... investigate Gaussian Kullback-Leibler (G-KL) variational approximate inference techniques for Bayesian generalised linear models and various ...of Gaussian covariance that make G-KL methods fast and ...

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Continuous and simultaneous estimation of finger kinematics using inputs from an EMG-to-muscle activation model

Continuous and simultaneous estimation of finger kinematics using inputs from an EMG-to-muscle activation model

... a fast feedforward artificial neural network and a nonparamet- ric Gaussian Process regression [16], with the latter having the potential to give better estimation performance but rarely used ...

14

Adaptive CSI and feedback estimation in LTE and beyond: a Gaussian process regression approach

Adaptive CSI and feedback estimation in LTE and beyond: a Gaussian process regression approach

... In order to obtain a good estimate of the future measure and its underlying distribution, a covariance function that best fits the nature of the system has to be selected. As the mean can easily be set to zero if some ...

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Patchwork Kriging for Large-scale Gaussian Process Regression

Patchwork Kriging for Large-scale Gaussian Process Regression

... concerns fast computation of the GP regression estimate for large N ...GP regression is the inversion of a N × N sample covariance matrix, which is also often poorly numerically ...a Gaussian ...

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Tracking by fusion via Gaussian Process Regression extended to transfer learning

Tracking by fusion via Gaussian Process Regression extended to transfer learning

... sampling process more efficiently as the values are only associated with each particle’s location and scale in the current ...This process encourages the particles to be in the right place both for our GP ...

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Efficient Computation of Gaussian Process Regression for Large Spatial Data Sets by Patching Local Gaussian Processes

Efficient Computation of Gaussian Process Regression for Large Spatial Data Sets by Patching Local Gaussian Processes

... GP regression has received much attention in machine learn- ing and spatial ...GP regression is the inversion of a big sample covariance matrix of size N × N , many approaches proposed to approximate the ...

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Gaussian process approximations for fast inference from infectious disease data

Gaussian process approximations for fast inference from infectious disease data

... of Gaussian process approximations, including those based on stochastic moment closure (which have branching process results as a special case) and those based on a linear time-inhomogeneous SDE ...

24

A spatio temporal, Gaussian Process Regression, real estate price predictor

A spatio temporal, Gaussian Process Regression, real estate price predictor

... number of points [10]. The covariance function (kernel) cho- sen was a Radial Basis (Gaussian) function. The reason for including this extra step, rather than undertaking Kriging with an external drift, was to ...

5

Determination of Strain Energy for Triggering Liquefaction Based on Gaussian Process Regression

Determination of Strain Energy for Triggering Liquefaction Based on Gaussian Process Regression

... This study adopts Gaussian process regression (GPR) for prediction of amount of strain energy required to induce liquefaction. In GPR, the learning of data is modeled as Bayesian estimation problem. ...

8

Spatio-Temporal Structured Sparse Regression With Hierarchical Gaussian Process Priors

Spatio-Temporal Structured Sparse Regression With Hierarchical Gaussian Process Priors

... specific knowledge about the structure such as determination of particular groups of coefficients with similar behaviour. If, however, there is information about the structure, it can be easily incorporated into the ...

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Modeling Daily Pan Evaporation in Humid Climates Using Gaussian Process Regression

Modeling Daily Pan Evaporation in Humid Climates Using Gaussian Process Regression

... study, Gaussian Process Regression (GPR), Nearest-Neighbor (IBK), Random Forest (RF) and Support Vector Regression (SVR) were used to estimate the pan evaporation (PE) in the meteorological ...

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A Gaussian process regression for natural gas consumption prediction based on time series data

A Gaussian process regression for natural gas consumption prediction based on time series data

... autoregressive Gaussian Process models phase is carried out using the Algerian natural gas market data together with exogenous inputs consisting in weather (tempera- ture) and calendar (day of the week, ...

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Gaussian Processes for Ordinal Regression

Gaussian Processes for Ordinal Regression

... metric regression prob- ...The Gaussian kernel (1) was used in these three ...Our Gaussian process algorithms often yield better results ...

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Statistical Methods for Computer Experiments with Applications in Neuroscience

Statistical Methods for Computer Experiments with Applications in Neuroscience

... laGP is an R package (Gramacy, 2016) that provides approximate GP regression for extensive computer experiments and spatial databases. The approximation method finds small local designs (independently) to predict ...

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Random Walk Kernels and Learning Curves for Gaussian Process Regression on Random Graphs

Random Walk Kernels and Learning Curves for Gaussian Process Regression on Random Graphs

... like Gaussian processes capture prior correlations between points in an input space by mapping to a higher dimensional space, where correlations can be represented by a linear combination of ‘features’ (see, ...

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Soft Sensor Development for the Measurement of CO2

Soft Sensor Development for the Measurement of CO2

... Soft sensor or virtual sensor is a common name for softwarethat is used to process and analyze several measurements together.Soft sensors can deal with measurements numbering in the dozens or the hundreds. ...

5

GPLP: A Local and Parallel Computation Toolbox for Gaussian Process Regression

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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