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Title: Slice sampling covariance hyperparameters of latent Gaussian models
Authors: Murray, Iain
Adams, Ryan Prescott
Issue Date: 2010
Journal Title: Neural Information Processing Systems Conference 2010
Abstract: The Gaussian process (GP) is a popular way to specify dependencies between 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 explanations for the data when making predictions. This integration is often performed 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.
URI: http://homepages.inf.ed.ac.uk/imurray2/pub/10hypers/hypers.pdf
http://hdl.handle.net/1842/4584
Appears in Collections:Informatics Publications

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