@book{Rasmussen2006,
  title                    = {Gaussian Processes for Machine Learning},
  author                   = {Rasmussen, Carl E. and Williams, Christopher K. I.},
  editor                   = {Dietterich, T. G.},
  publisher                = {The MIT Press},
  year                     = {2006},
  address                  = {Cambridge, MA, USA},
  series                   = {Adaptive Computation and Machine Learning},
  url                      = {http://www.gaussianprocess.org/gpml/chapters/RW.pdf}
}
@article{Görtler2019,
  author = {Görtler, Jochen and Kehlbeck, Rebecca and Deussen, Oliver},
  title = {A Visual Exploration of Gaussian Processes},
  journal = {Distill},
  year = {2019},
  note = {https://distill.pub/2019/visual-exploration-gaussian-processes},
  doi = {10.23915/distill.00017}
}
@TechReport{Brochu2009,
  Title                    = {A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning},
  Author                   = {Brochu, Eric and Cora, Vlad M. and de Freitas, Nando},
  Institution              = {Department of Computer Science, University of British Columbia},
  Year                     = {2009},
  Number                   = {TR-2009-023},
  Url                      = {http://haikufactory.com/files/bayopt.pdf}
}
@article{Gershgorin1931,
  title={Über die Abgrenzung der Eigenwerte einer Matrix},
  author={Gershgorin, Semyon Aranovich},
  journal={Izvestija Akademii Nauk SSSR},
  volume={6},
  number={3},
  pages={749—754},
  year={1931},
  series = {Serija Matematika},
  url = {http://www.mathnet.ru/links/1ac35374c9ae21fd05065caf931b9a15/im5235.pdf}
}
@Article{Deisenroth2015,
  Title                    = {Gaussian Processes for Data-Efficient Learning in Robotics and Control},
  Author                   = {Deisenroth, Marc P. and Fox, Dieter and Rasmussen, Carl E.},
  Journal                  = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
  Year                     = {2015},
  Month                    = {February},
  Number                   = {2},
  Pages                    = {408—423},
  Volume                   = {37},
  Doi                      = {10.1109/TPAMI.2013.218},
  Url                      = {http://ieeexplore.ieee.org/document/6654139/}
}
@Book{Scholkopf2002,
  Title                    = {Learning with Kernels—Support Vector Machines, Regularization, Optimization, and Beyond},
  Author                   = {Schölkopf, Bernhard and Smola, Alexander J.},
  Editor                   = {Dietterich, T. G.},
  Publisher                = {The MIT Press},
  Year                     = {2002},
  Address                  = {Cambridge, MA, USA},
  Series                   = {Adaptive Computation and Machine Learning},
  Url                      = {http://agbs.kyb.tuebingen.mpg.de/lwk/}
}
@InCollection{MacKay1998,
  Title                    = {Introduction to Gaussian Processes},
  Author                   = {MacKay, David J. C.},
  Booktitle                = {Neural Networks and Machine Learning},
  Publisher                = {Springer},
  Year                     = {1998},
  Address                  = {Berlin, Germany},
  Editor                   = {Bishop, C. M.},
  Pages                    = {133—165},
  Volume                   = {168},
  Url                      = {http://www.cs.utoronto.ca/~mackay/gpB.pdf}
}
@Book{MacKay2003,
  Title                    = {Information Theory, Inference, and Learning Algorithms},
  Author                   = {MacKay, Davic J. C.},
  Publisher                = {Cambridge University Press},
  Year                     = {2003},
  Address                  = {The Edinburgh Building, Cambridge CB2 2RU, UK},
  URL                      = {http://www.inference.org.uk/mackay/itila/book.html}
}
@Misc{gpjs2014,
  author =   {Peltola, T.},
  title =    {Gaussian Process Regression Demo in Javascript},
  url = {https://github.com/to-mi/gp-demo-js},
  year = {2014}
}

@InProceedings{Rasmussen2001,
  Title                    = {Occam's Razor},
  Author                   = {Rasmussen, Carl E. and Ghahramani, Zoubin},
  Booktitle                = {Advances in Neural Information Processing Systems},
  Year                     = {2001},
  Pages                    = {294—300},
  Publisher                = {The MIT Press},
  URL = {http://www.gatsby.ucl.ac.uk/~edward/pub/occam.pdf}
}
@article{Matthews2017,
   title={GPflow: A Gaussian Process Library using TensorFlow},
   author={Matthews, Alexander G. de G. and Wilk, Mark van der and Nickson, Tom and Fujii, Keisuke and Boukouvalas, Alexis and Leon-Villagra, Pablo and Ghahramani, Zoubin and Hensman, James},
  journal = {Journal of Machine Learning Research},
  year    = {2017},
  volume  = {18},
  number  = {40},
  pages   = {1—6},
  url     = {http://jmlr.org/papers/v18/16-537.html}
}
@article{Vanhatalo2013,
   title={GPstuff: Bayesian Modeling with Gaussian Processes},
   author={Jarno Vanhatalo and Jaakko Riihimäki and Jouni Hartikainen and Pasi Jylänki and Ville Tolvanen and Aki Vehtari},
  journal = {Journal of Machine Learning Research},
  year    = {2013},
  volume  = {14},
  number  = {40},
  pages   = {1175—1179},
  url     = {http://jmlr.org/papers/v14/vanhatalo13a.html}
}
@article{Rasmussen2010,
   title={Gaussian Processes for Machine Learning (GPML) Toolbox},
   author={Carl E. Rasmussen and Hannes Nickisch},
  journal = {Journal of Machine Learning Research},
  year    = {2010},
  volume  = {11},
  pages   = {3011—3015},
  url     = {http://jmlr.org/papers/v11/rasmussen10a.html}
}
@Misc{gpy2014,
  author =   {GPy},
  title =    {GPy: A Gaussian Process Framework in Python},
  url = {https://github.com/SheffieldML/GPy},
  year = {since 2012}
}
@article{Neumann2015,
  author  = {Marion Neumann and Shan Huang and Daniel E. Marthaler and Kristian Kersting},
  title   = {pyGPs—A Python Library for Gaussian Process Regression and Classification},
  journal = {Journal of Machine Learning Research},
  year    = {2015},
  volume  = {16},
  pages   = {2611-2616},
  url     = {http://jmlr.org/papers/v16/neumann15a.html}
}
@TechReport{Brochu2009,
  Title                    = {A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning},
  Author                   = {Brochu, Eric and Cora, Vlad M. and de Freitas, Nando},
  Institution              = {Department of Computer Science, University of British Columbia},
  Year                     = {2009},
  Number                   = {TR-2009-023},
  Url                      = {http://haikufactory.com/files/bayopt.pdf}
}
@Article{Shahriari2016,
  Title                    = {Taking the Human out of the Loop: A Review of Bayesian Optimization},
  Author                   = {Shahriari, Bobak and Swersky, Kevin and Wang, Ziyu and Adams, Ryan P. and de Freitas, Nando},
  Journal                  = {Proceedings of the IEEE},
  Year                     = {2016},
  Month                    = {January},
  Number                   = {1},
  Pages                    = {148—175},
  Volume                   = {104}
}
@InProceedings{Osborne2009,
  Title                    = {Gaussian Processes for Global Optimization},
  Author                   = {Osborne, Michael A. and Garnett, Roman and Roberts, Stephen J.},
  Booktitle                = {Proceedings of the International Conference on Learning and Intelligent Optimization},
  Year                     = {2009},
  Url                      = {http://www.robots.ox.ac.uk/~mosb/papers/OsborneGarnettRobertsGPGO.pdf}
}
@InProceedings{Wagberg2017,
  author    = {Johan Wågberg and Dave Zachariah and Thomas Schön and Petre Stoica},
  title     = {Prediction Performance After Learning in Gaussian Process Regression},
  booktitle = {Proceedings of the International Conference on Artificial Intelligence and Statistics},
  year      = {2017},
  editor    = {Aarti Singh and Jerry Zhu},
  volume    = {54},
  series    = {Proceedings of Machine Learning Research},
  publisher = {PMLR},
  month     = {April},
  pages     = {1264—1272},
  url       = {http://proceedings.mlr.press/v54/wagberg17a.html},
  abstract  = {This paper considers the quantification of the prediction performance in Gaussian process regression. The standard approach is to base the prediction error bars on the theoretical predictive variance, which is a lower bound on the mean square-error (MSE). This approach, however, does not take into account that the statistical model is learned from the data. We show that this omission leads to a systematic underestimation of the prediction errors. Starting from a generalization of the Cramér-Rao bound, we derive a more accurate MSE bound which provides a measure of uncertainty for prediction of Gaussian processes. The improved bound is easily computed and we illustrate it using synthetic and real data examples.},
  pdf       = {http://proceedings.mlr.press/v54/wagberg17a/wagberg17a.pdf},
  }
@online{DuvenaudKernelCookbook,
  author = {David Duvenaud},
  title = {http://www.cs.toronto.edu/~duvenaud/cookbook/index.html},
  year = 2013,
  url = {http://www.cs.toronto.edu/~duvenaud/cookbook/index.html},
  urldate = {2017-28-11}
}