Gaussian Processes for Machine Learning (GPML) Toolbox

@article{Rasmussen2010GaussianPF,
  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}
}
The GPML toolbox provides a wide range of functionality for G aussian process (GP) inference and prediction. GPs are specified by mean and covariance func tions; we offer a library of simple mean and covariance functions and mechanisms to compose mor e complex ones. Several likelihood functions are supported including Gaussian and heavytailed for regression as well as others suitable for classification. Finally, a range of inference m thods is provided, including exact and variational inference… CONTINUE READING
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Minka . Expectation propagation for approximate Bayesian inference

  • P Thomas
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