@article{3753,
  title = {A Unifying View of Sparse Approximate Gaussian Process Regression},
  journal = {Journal of Machine Learning Research},
  abstract = {We provide a new unifying view, including all existing proper probabilistic
  sparse approximations for Gaussian process regression. Our approach relies on
  expressing the effective prior which the methods are using. This
  allows new insights to be gained, and highlights the relationship between
  existing methods. It also allows for a clear theoretically justified ranking
  of the closeness of the known approximations to the corresponding full GPs.
  Finally we point directly to designs of new better sparse approximations,
  combining the best of the existing strategies, within attractive
  computational constraints.},
  volume = {6},
  pages = {1935-1959},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = dec,
  year = {2005},
  author = {Quinonero Candela, J. and Rasmussen, CE.},
  month_numeric = {12}
}