A comparative evaluation of stochastic-based inference methods for Gaussian process models

M. Filippone*, M. Zhong, M. Girolami

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

41 Scopus citations

Abstract

Gaussian Process (GP) models are extensively used in data analysis given their flexible modeling capabilities and interpretability. The fully Bayesian treatment of GP models is analytically intractable, and therefore it is necessary to resort to either deterministic or stochastic approximations. This paper focuses on stochastic-based inference techniques. After discussing the challenges associated with the fully Bayesian treatment of GP models, a number of inference strategies based on Markov chain Monte Carlo methods are presented and rigorously assessed. In particular, strategies based on efficient parameterizations and efficient proposal mechanisms are extensively compared on simulated and real data on the basis of convergence speed, sampling efficiency, and computational cost.

Original languageEnglish (US)
Pages (from-to)93-114
Number of pages22
JournalMachine Learning
Volume93
Issue number1
DOIs
StatePublished - Oct 2013

Keywords

  • Bayesian inference
  • Gaussian processes
  • Hierarchical models
  • Latent variable models
  • Markov chain Monte Carlo

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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