Slice sampling covariance hyperparameters of latent Gaussian models

Details Slice sampling covariance hyperparameters of latent Gaussian models Slice sampling covariance hyperparameters of latent Gaussian models

2010-06-04

arxiv.org/abs/1006.0868v2

Statistics

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9 pages, 4 figures, 4 algorithms. Minor corrections to previous version. This version to appear in Advances in Neural Informat

Scientific paper

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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