@article{5305,
  title = {Approximations for Binary Gaussian Process Classification},
  journal = {Journal of Machine Learning Research},
  abstract = {We provide a comprehensive overview of many recent algorithms for approximate inference in
  Gaussian process models for probabilistic binary classification. The relationships between several
  approaches are elucidated theoretically, and the properties of the different algorithms are
  corroborated by experimental results. We examine both 1) the quality of the predictive distributions and
  2) the suitability of the different marginal likelihood approximations for model selection (selecting
  hyperparameters) and compare to a gold standard based on MCMC. Interestingly, some methods
  produce good predictive distributions although their marginal likelihood approximations are poor.
  Strong conclusions are drawn about the methods: The Expectation Propagation algorithm is almost
  always the method of choice unless the computational budget is very tight. We also extend
  existing methods in various ways, and provide unifying code implementing all approaches.},
  volume = {9},
  pages = {2035-2078},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = oct,
  year = {2008},
  slug = {5305},
  author = {Nickisch, H. and Rasmussen, CE.},
  month_numeric = {10}
}