Elliptical slice sampling

Iain Murray, Ryan Prescott Adams, David J. C. MacKay

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.
Original languageEnglish
Title of host publicationProceedings of the 13th International Conference on Artificial Intelligence and Statistics (AISTATS)
PublisherJournal of Machine Learning Research: Workshop and Conference Proceedings
Pages541-548
Number of pages8
Publication statusPublished - 2010

Publication series

NameJournal of Machine Learning Research: Workshop and Conference Proceedings
Volume9

Fingerprint

Dive into the research topics of 'Elliptical slice sampling'. Together they form a unique fingerprint.

Cite this