GPML

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 11 articles , 1 standard article )

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  1. Mooij, Joris M.; Peters, Jonas; Janzing, Dominik; Zscheischler, Jakob; Schölkopf, Bernhard: Distinguishing cause from effect using observational data: methods and benchmarks (2016)
  2. Bouveyron, C.; Fauvel, M.; Girard, S.: Kernel discriminant analysis and clustering with parsimonious Gaussian process models (2015)
  3. Neumann, Marion; Huang, Shan; Marthaler, Daniel E.; Kersting, Kristian: pyGPS -- a python library for Gaussian process regression and classification (2015)
  4. Couckuyt, Ivo; Dhaene, Tom; Demeester, Piet: ooDACE toolbox: a flexible object-oriented Kriging implementation (2014)
  5. Desautels, Thomas; Krause, Andreas; Burdick, Joel W.: Parallelizing exploration-exploitation tradeoffs in Gaussian process bandit optimization (2014)
  6. Martinez-Cantin, Ruben: BayesOpt: a Bayesian optimization library for nonlinear optimization, experimental design and bandits (2014)
  7. Peters, Jonas; Mooij, Joris M.; Janzing, Dominik; Schölkopf, Bernhard: Causal discovery with continuous additive noise models (2014)
  8. Přikryl, Jan: Graphics card as a cheap supercomputer. (2013)
  9. Vanhatalo, Jarno; Riihimäki, Jaakko; Hartikainen, Jouni; Jylänki, Pasi; Tolvanen, Ville; Vehtari, Aki: GPstuff: Bayesian modeling with Gaussian processes (2013)
  10. Janzing, Dominik; Mooij, Joris; Zhang, Kun; Lemeire, Jan; Zscheischler, Jakob; Daniušis, Povilas; Steudel, Bastian; Schölkopf, Bernhard: Information-geometric approach to inferring causal directions (2012)
  11. Rasmussen, Carl Edward; Nickisch, Hannes: Gaussian processes for machine learning (GPML) toolbox (2010)