Elliptical slice sampling

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

Research output: Contribution to journalConference articlepeer-review

124 Scopus citations

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 (US)
Pages (from-to)541-548
Number of pages8
JournalJournal of Machine Learning Research
Volume9
StatePublished - Dec 1 2010
Externally publishedYes
Event13th International Conference on Artificial Intelligence and Statistics, AISTATS 2010 - Sardinia, Italy
Duration: May 13 2010May 15 2010

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Statistics and Probability
  • Artificial Intelligence

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