GPy is a Gaussian Process (GP) framework written in python, from the Sheffield machine learning group. Gaussian processes underpin range of modern machine learning algorithms. In GPy, we’ve used python to implement a range of machine learning algorithms based on GPs. GPy is available under the BSD 3-clause license.
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References in zbMATH (referenced in 4 articles )
Showing results 1 to 4 of 4.
- Damianou, Andreas C.; Titsias, Michalis K.; Lawrence, Neil D.: Variational inference for latent variables and uncertain inputs in Gaussian processes (2016)
- Samo, Yves-Laurent Kom; Roberts, Stephen J.: String and membrane Gaussian processes (2016)
- Tripathy, Rohit; Bilionis, Ilias; Gonzalez, Marcial: Gaussian processes with built-in dimensionality reduction: applications to high-dimensional uncertainty propagation (2016)
- Neumann, Marion; Huang, Shan; Marthaler, Daniel E.; Kersting, Kristian: pyGPs -- a Python library for Gaussian process regression and classification (2015)