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Assessing Approximate Inference for Binary Gaussian Process Classification




Gaussian process priors can be used to define flexible, probabilistic classification models. Unfortunately exact Bayesian inference is analytically intractable and various approximation techniques have been proposed. In this work we review and compare Laplace‘s method and Expectation Propagation for approximate Bayesian inference in the binary Gaussian process classification model. We present a comprehensive comparison of the approximations, their predictive performance and marginal likelihood estimates to results obtained by MCMC sampling. We explain theoretically and corroborate empirically the advantages of Expectation Propagation compared to Laplace‘s method.

Author(s): Kuss, M. and Rasmussen, C.
Journal: Journal of Machine Learning Research
Volume: 6
Pages: 1679
Year: 2005
Month: October
Day: 0

Department(s): Empirical Inference
Bibtex Type: Article (article)

Digital: 0
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF


  title = {Assessing Approximate Inference for Binary Gaussian Process Classification},
  author = {Kuss, M. and Rasmussen, C.},
  journal = {Journal of Machine Learning Research},
  volume = {6},
  pages = {1679 },
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = oct,
  year = {2005},
  month_numeric = {10}