PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Slice sampling covariance hyperparameters of latent Gaussian models
Iain Murray and Ryan Adams
In: Neural Information Processing Systems (NIPS) 23, Vancouver, Canada(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.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
ID Code:7223
Deposited By:Iain Murray
Deposited On:10 March 2011