Heteroscedastic regression considering the varying noises among observations has many applications in fields, such as machine learning and statistics. Here, we focus on heteroscedastic Gaussian process (HGP) that integrates latent function noise a unified nonparametric Bayesian framework. Though showing remarkable performance, HGP suffers from cubic time complexity, which strictly limits its ap...

We present MOGPTK, a Python package for multi-channel data modelling using Gaussian processes (GP). The aim of this toolkit is to make multi-output GP (MOGP) models accessible researchers, scientists, and practitioners alike. MOGPTK uses front-end relies on the PyTorch suite, thus enabling GPU-accelerated training. facilitates implementing entire pipeline modelling, including loading, parameter...

The modeling and simulation of dynamical systems is a necessary step for many control approaches. Using classical, parameter-based techniques modern systems, e.g., soft robotics or human–robot interaction, are often challenging even infeasible due to the complexity system dynamics. In contrast, data-driven approaches need only minimum prior knowledge scale with system. particular, Gaussian proc...

Abstract Gaussian Process (GP) regression models typically assume that residuals are Gaussian and have the same variance for all observations. However, applications with input-dependent noise (heteroscedastic residuals) frequently arise in practice, as do applications in which the residuals do not have a Gaussian distribution. In this paper, we propose a GP Regression model with a latent variab...