PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Variational inference in Gaussian processes via probabilistic point assimilation
Nathaniel King and Neil Lawrence
(2005) Technical Report. University of Sheffield, Sheffield, UK.

Abstract

Abstract: We introduce a novel variational approach for approximate inference in Gaussian process (GP) models. The key advantages of our approach are the ease with which different noise models can be incorporated and improved speed of convergence. We refer to the algorithm as probabilistic point assimilation (PPA). We introduce the algorithm firstly using the `weight space' view and then through its Gaussian process formulation. We illustrate the approach on several benchmark data sets.

Postscript - Requires a viewer, such as GhostView
EPrint Type:Monograph (Technical Report)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:1218
Deposited By:Nathaniel King
Deposited On:28 November 2005