Abstract—This paper presents a Gaussian process model-based short-term electric load forecasting. The Gaussian process model is a nonparametric model and the output of the model has Gaussian distribution with mean and variance. The multiple Gaussian process models as every hour ahead predictors

We consider a Gaussian process formulation of the multiple kernel learning problem. The goal is to select the convex combination of kernel matrices that best explains the data and by doing so improve the generalisation on unseen data. Sparsity in the kernel weights is obtained by adopting a

This paper aims to model relational data on edges of networks. We describe appropriate Gaussian Processes (GPs) for directed, undirected, and bipartite networks. The inter-dependencies of edges can be effectively modeled by adapting the GP hyper-parameters. The framework suggests an intimate

Abstract — The paper describes the identification of nonlinear dynamic systems with a Gaussian process prior model. This approach is an example of a probabilistic, non-parametric modelling. Gaussian process model can be considered as the special case of radial basis function network

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The primary goal is to develop a response surface for a variety of flight conditions. • Running a standard experiment is infeasible • Wind tunnel experiments are expensive • Computing is relatively cheap • Mathematical sophistication is increasing Thus a computer experiment is usedRocket Booster Simulations Inputs • speed (Mach number) • angle of attack (alpha) • side slip angle (beta) Outputs • lift • drag • pitch

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We propose a family of multivariate Gaussian process models for correlated out-puts, based on assuming that the likelihood function takes the generic form of the multivariate exponential family distribution (EFD). We denote this model as a multivariate generalized Gaussian process model, and derive

This paper describes model-based predictive control based on Gaussian processes. Gaussian process models provide a probabilistic nonparametric modelling approach for black-box identification of non-linear dynamic systems. It offers more insight in variance of obtained model response, as well

The Gaussian process model is an example of a flexible, probabilistic, nonparametric model with uncertainty predictions. It offers a range of advantages for modelling from data and has been therefore used also for dynamic systems identification. One of the noticeable drawbacks of the system