Gaussian Process

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.

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.