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 ...
evaluation,theproposedMA—GPRmodelsignificantlyimprovedthepredictionaccuracy ,comparedwiththeconjugategradient methodandthegeneticalgorithmoptimizationprocess. Keywords:Gaussianprocess;hyper-parametersoptimization;memeticalgorithm;regressionmodel lIntr0ducti0n Beinganewkernelmethoddevelopedonthebasis ...
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...
Therefore, we offer Gaussian process regression (GPR) to accommodate the issue. In this paper, we will focus on spatial modeling with GPR for binomial data with logit link function. The performance of the model will be investigated. We will discuss the inference of how to estimate the ...
We present LonGP, an additive Gaussian process regression model that is specifically designed for statistical analysis of longitudinal data, which solves these commonly faced challenges. LonGP can model time-varying random effects and non-stationary signals, incorporate multiple kernel learning, and ...
Next, the highly influential FMGPR models are selected using genetic algorithm (GA) based ensemble pruning. When a new test sample comes, the component predictions from the selected FMGPR models are adaptively combined by using FMM again and the probabilistic prediction results of the SEFMGPR ...
most transductive approaches consider the case of classification only. In this paper we introduce a transductive variant of Gaussian process regression with automatic model selection, based on approximate moment matching between training and test data. Empirical results show the feasibility and competitivenes...
This selection reduces the number of eigenfunctions in our model and provides a nonstationary covariance function. To handle nonlinear likelihoods, we develop an efficient expectation propagation (EP) inference algorithm, and couple it with expectation maximization for eigenfunction selection. Because the ...
First we perform a full Gaussian process regression on the data. We create a GP model,m_full, and fit it to the data, plotting the resulting fit. m_full=GPy.models.GPRegression(X,y) _=m_full.optimize(messages=True)# Optimize parameters of covariance function ...
In response, we develop a drift controller based on Model Predictive Control (MPC) and incorporate Gaussian Process Regression to correct discrepancies in the vehicle dynamics model. Moreover, the covariance from the GPR is employed to actively explore various cornering states, aiming to minimize ...