Gaussian Process

Multivariate {G}aussian processes: definitions, examples and applications

In this paper, we propose a precise definition of multivariate Gaussian processes based on Gaussian measures on vector-valued function spaces, and provide an existence proof.

Multivariate Gaussian and Student-t process regression for multioutput prediction

In this paper, we propose a unified framework which is used not only to introduce a novel multivariate Student-t process regression model (MV-TPR) for multi-output prediction, but also to reformulate the multivariate Gaussian process regression (MV-GPR) that overcomes some limitations of the existing methods.

Welcome to Our Research Interest Group

Welcome to our Gaussian Process Research Interest Group (approx. 500 members), including Bayesian non-parametric modelling, Bayesian optimisation, Bayesian inference, Bayesian computation and many related application areas. [**Join Us via Slack**]( !!!

Gaussian process regression methods and extensions for stock market prediction

Gaussian process regression (GPR) is a kernel-based nonparametric method that has been proved to be effective and powerful in many areas, including time series prediction. In this thesis, we focus on GPR and its extensions and then apply them to …

How priors of initial hyperparameters affect Gaussian process regression models

In this paper, we provide the first empirical study on the problem of how prior affect Gaussian process regression using simulated and real data experiments.