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Summary: Elliptical slice sampling
Iain Murray Ryan Prescott Adams David J.C. MacKay
University of Toronto University of Toronto University of Cambridge
Abstract
Many probabilistic models introduce strong
dependencies between variables using a latent
multivariate Gaussian distribution or a Gaus-
sian process. We present a new Markov chain
Monte Carlo algorithm for performing infer-
ence 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.
1 Introduction
The multivariate Gaussian distribution is commonly
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