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.
@conference{murray2010elliptical,
year = {2010},
author = {Murray, Iain and Adams, Ryan P. and MacKay, David J.C.},
title = {Elliptical Slice Sampling},
booktitle = {Proceedings of the 13th International Conference on Artificial Intelligence and Statistics (AISTATS)},
note = {arXiv:1001.0175 [stat.CO]},
keywords = {Gaussian processes, Bayesian methods, Markov chain Monte Carlo, AISTATS}
}