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Slice sampling covariance hyperparameters of latent Gaussian models

Summary: Slice sampling covariance hyperparameters
of latent Gaussian models
Iain Murray
School of Informatics
University of Edinburgh
Ryan Prescott Adams
Dept. Computer Science
University of Toronto
The Gaussian process (GP) is a popular way to specify dependencies be-
tween random variables in a probabilistic model. In the Bayesian framework
the covariance structure can be specified using unknown hyperparameters.
Integrating over these hyperparameters considers different possible expla-
nations for the data when making predictions. This integration is often per-
formed using Markov chain Monte Carlo (MCMC) sampling. However, with
non-Gaussian observations standard hyperparameter sampling approaches
require careful tuning and may converge slowly. In this paper we present
a slice sampling approach that requires little tuning while mixing well in
both strong- and weak-data regimes.
1 Introduction


Source: Adams, Ryan Prescott - Department of Electrical and Computer Engineering, University of Toronto
Edinburgh, University of - Division of Informatics, Institute for Adaptive and Neural Computation
Roweis, Sam - Department of Computer Science, University of Toronto


Collections: Biology and Medicine; Computer Technologies and Information Sciences