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.
Keywords for this software
References in zbMATH (referenced in 5 articles )
Showing results 1 to 5 of 5.
- 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)
- 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)