Gaussian processes for machine learning (GPML) toolbox. The GPML toolbox provides a wide range of functionality for Gaussian process (GP) inference and prediction. GPs are specified by mean and covariance functions; we offer a library of simple mean and covariance functions and mechanisms to compose more complex ones. Several likelihood functions are supported including Gaussian and heavy-tailed for regression as well as others suitable for classification. Finally, a range of inference methods is provided, including exact and variational inference, Expectation Propagation, and Laplace’s method dealing with non-Gaussian likelihoods and FITC for dealing with large regression tasks.

References in zbMATH (referenced in 19 articles , 1 standard article )

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  1. Bussas, Matthias; Sawade, Christoph; Kühn, Nicolas; Scheffer, Tobias; Landwehr, Niels: Varying-coefficient models for geospatial transfer learning (2017)
  2. Ghosh, Sanmitra; Dasmahapatra, Srinandan; Maharatna, Koushik: Fast approximate Bayesian computation for estimating parameters in differential equations (2017)
  3. Li, Yongqiang; Hou, Zhongsheng; Feng, Yuanjing; Chi, Ronghu: Data-driven approximate value iteration with optimality error bound analysis (2017)
  4. Matthews, Alexander G.De G.; van der Wilk, Mark; Nickson, Tom; Fujii, Keisuke; Boukouvalas, Alexis; León-Villagrá, Pablo; Ghahramani, Zoubin; Hensman, James: GPflow: a Gaussian process library using tensorflow (2017)
  5. Pang, Guofei; Perdikaris, Paris; Cai, Wei; Karniadakis, George Em: Discovering variable fractional orders of advection-dispersion equations from field data using multi-fidelity Bayesian optimization (2017)
  6. Belyaev, Mikhail; Burnaev, Evgeny; Kapushev, Y.: Computationally efficient algorithm for Gaussian process regression in case of structured samples (2016)
  7. Mooij, Joris M.; Peters, Jonas; Janzing, Dominik; Zscheischler, Jakob; Schölkopf, Bernhard: Distinguishing cause from effect using observational data: methods and benchmarks (2016)
  8. Bouveyron, C.; Fauvel, M.; Girard, S.: Kernel discriminant analysis and clustering with parsimonious Gaussian process models (2015)
  9. Neumann, Marion; Huang, Shan; Marthaler, Daniel E.; Kersting, Kristian: pyGPs -- a Python library for Gaussian process regression and classification (2015)
  10. Babtie, Ann C.; Kirk, Paul; Stumpf, Michael P.H.: Topological sensitivity analysis for systems biology (2014)
  11. Couckuyt, Ivo; Dhaene, Tom; Demeester, Piet: ooDACE toolbox: a flexible object-oriented Kriging implementation (2014)
  12. Desautels, Thomas; Krause, Andreas; Burdick, Joel W.: Parallelizing exploration-exploitation tradeoffs in Gaussian process bandit optimization (2014)
  13. Martinez-Cantin, Ruben: BayesOpt: a Bayesian optimization library for nonlinear optimization, experimental design and bandits (2014)
  14. Peters, Jonas; Mooij, Joris M.; Janzing, Dominik; Schölkopf, Bernhard: Causal discovery with continuous additive noise models (2014)
  15. Pau, George Shu Heng; Zhang, Yingqi; Finsterle, Stefan: Reduced order models for many-query subsurface flow applications (2013)
  16. Přikryl, Jan: Graphics card as a cheap supercomputer. (2013)
  17. Vanhatalo, Jarno; Riihimäki, Jaakko; Hartikainen, Jouni; Jylänki, Pasi; Tolvanen, Ville; Vehtari, Aki: GPstuff: Bayesian modeling with Gaussian processes (2013)
  18. Janzing, Dominik; Mooij, Joris; Zhang, Kun; Lemeire, Jan; Zscheischler, Jakob; Daniušis, Povilas; Steudel, Bastian; Schölkopf, Bernhard: Information-geometric approach to inferring causal directions (2012)
  19. Rasmussen, Carl Edward; Nickisch, Hannes: Gaussian processes for machine learning (GPML) toolbox (2010)