GPstuff: Bayesian modeling with Gaussian processes

Authors:
Jarno Vanhatalo;Jaakko Riihimäki;Jouni Hartikainen;Pasi Jylänki;Ville Tolvanen;Aki Vehtari
Affiliations:
Department of Environmental Sciences, University of Helsinki, Helsinki, Finland;Department of Biomedical Engineering and Computational Science, Aalto University School of Science, Aalto, Finland;Department of Biomedical Engineering and Computational Science, Aalto University School of Science, Aalto, Finland;Department of Biomedical Engineering and Computational Science, Aalto University School of Science, Aalto, Finland;Department of Biomedical Engineering and Computational Science, Aalto University School of Science, Aalto, Finland;Department of Biomedical Engineering and Computational Science, Aalto University School of Science, Aalto, Finland
Venue:
The Journal of Machine Learning Research
Year:
2013

Citing 4
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Abstract

The GPstuff toolbox is a versatile collection of Gaussian process models and computational tools required for Bayesian inference. The tools include, among others, various inference methods, sparse approximations and model assessment methods.