Gaussianprocess.org Website Review

This web site aims to provide an overview of resources concerned with probabilistic modeling, inference and learning based on Gaussian processes. Although Gaussian processes have a long history in the field of statistics, they seem to have been employed extensively only in niche areas. With the advent of kernel machines in the machine learning community, models based on Gaussian processes have become commonplace for problems of regression (kriging) and clas*ification as well as a host of more specialized applications. An extension of the Netlab implementation for GP regression. It allows large scale regression based on the BCM approximation, see also the An extensive and well docu*ented package implementing Markov chain Monte Carlo methods for Bayesian inference in neural networks, Gaussian processes (regression, binary and multi-clas* clas*ification), mixture models and Dirichlet Diffusion trees. The GPML toolbox implements approximate inference algorithms for Gaussian processes such as Expectation Propagation, the Laplace Approximation and Variational Bayes for a wide clas* of likelihood functions for both regression and clas*ification. It comes with a big algebra of covariance and mean functions allowing for flexible modeling. The code is fully compatible to Octave 3.2.x. JMLR paper IVM Software in C++ , also includes the null category noise model for semi-supervised learning. Implements a Gaussian process interpretation of Kernel Fisher's discriminant. Software implementation of Gaussian Processes for Ordinal Regression. Provides Laplace Approximation, Expectation Propagation and A collection of matlab functions for Bayesian inference with Markov chain Monte Carlo (MCMC) methods. The purpose of this toolbox was to Approximate online learning in sparse Gaussian process models for regression (including several non-Gaussian likelihood functions) and Implements sparse GP regression as described in Sparse Gaussian Processes using Pseudo-inputs and Flexible and efficient Gaussian