arXiv:1505.02965v2 [math.ST] 29 Aug 2015 Page 1 Gaussian Processes for Regression: A
Quick Introduction M. Ebden, August 2008 Comments to mebden@gmail.com 1 MOTIVATION …

Even if you have spent some time reading about machine learning, chances are that you
have never heard of Gaussian processes. And if you have, rehearsing the basics is always a …

Gaussian processes (GPs) are natural generalisations of multivariate Gaussian random
variables to infinite (countably or continuous) index sets. GPs have been applied in a large …

We give a basic introduction to Gaussian Process regression models. We focus on
understanding the role of the stochastic process and how it is used to define a distribution …

This tutorial aims to provide an intuitive understanding of the Gaussian processes
regression. Gaussian processes regression (GPR) models have been widely used in …

The GPML toolbox provides a wide range of functionality for Gaussian process (GP)
inference and prediction. GPs are specified by mean and covariance functions; we offer a …

This tutorial aims to provide an intuitive introduction to Gaussian process regression (GPR).
GPR models have been widely used in machine learning applications due to their …

Gaussian processes (GPs) are flexible, simple to implement, fully probabilistic methods
suitable for a wide range of problems in regression and classification. A recent overview of …

We introduce stochastic variational inference for Gaussian process models. This enables the
application of Gaussian process (GP) models to data sets containing millions of data points …

Monge-Kantorovich distances, otherwise known as Wasserstein distances, have received a
growing attention in statistics and machine learning as a powerful discrepancy measure for …