PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Elliptical slice sampling
Iain Murray, Ryan Adams and David MacKay
The Proceedings of the 13th International Conference on Artificial Intelligence and Statistics (AISTATS) 2010.

Abstract

Many probabilistic models introduce strong dependencies between variables using a latent multivariate Gaussian distribution or a Gaussian process. We present a new Markov chain Monte Carlo algorithm for performing inference in models with multivariate Gaussian priors. Its key properties are: 1) it has simple, generic code applicable to many models, 2) it has no free parameters, 3) it works well for a variety of Gaussian process based models. These properties make our method ideal for use while model building, removing the need to spend time deriving and tuning updates for more complex algorithms.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:5941
Deposited By:Iain Murray
Deposited On:08 March 2010