Location of Repository

Slice sampling covariance hyperparameters of latent Gaussian models

By Iain Murray and Ryan Prescott Adams


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

Year: 2010
OAI identifier: oai:www.era.lib.ed.ac.uk:1842/4584

Suggested articles



  1. (1979). A note on the intervals between coal-mining disasters. doi
  2. (2005). Assessing approximate inference for binary Gaussian process classification.
  3. (2010). Auxiliary sampling using imaginary data,
  4. (1989). Classification of radar returns from the ionosphere using neural networks.
  5. (2009). Efficient sampling for Gaussian process inference using control variables. doi
  6. (2010). Elliptical slice sampling.
  7. (2001). Expectation propagation for approximate Bayesian inference.
  8. (2006). Gaussian Processes for machine learning. doi
  9. (1987). Hybrid Monte Carlo. doi
  10. (2000). Learning hyperparameters for neural network models using Hamiltonian dynamics.
  11. (1994). Markov chains for exploring posterior distributions. doi
  12. (2011). MCMC using Hamiltonian dynamics. doi
  13. (1977). Modelling spatial patterns. doi
  14. (1999). Regression and classification using Gaussian process priors.
  15. (2011). Riemann manifold Langevin and Hamiltonian Monte Carlo methods. doi
  16. (2006). Robust Markov chain Monte Carlo methods for spatial generalized linear mixed models. doi
  17. (2005). Slice sampling for simulation based fitting of spatial data models. doi
  18. (2003). Slice sampling. doi

To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.