The GPstuff toolbox is a versatile collection of Gaussian process models and computational tools required for inference. The tools include, among others, various inference methods, sparse approximations and model assessment methods. The GPstuff toolbox works (at least) with Matlab versions r2009b (7.9) or newer (older versions down to 7.7 should work also, but the code is not tested with them). Most of the functionality works also with Octave (3.6.4 or newer, see release notes for details). Most of the code is written in m-files but some of the most computationally critical parts have been coded in C.
Keywords for this software
References in zbMATH (referenced in 6 articles )
Showing results 1 to 6 of 6.
- Li, Longhai; Qiu, Shi; Zhang, Bei; Feng, Cindy X.: Approximating cross-validatory predictive evaluation in Bayesian latent variable models with integrated IS and WAIC (2016)
- Mononen, Tommi: A case study of the widely applicable Bayesian information criterion and its optimality (2015)
- Neumann, Marion; Huang, Shan; Marthaler, Daniel E.; Kersting, Kristian: pyGPs -- a Python library for Gaussian process regression and classification (2015)
- Yang, Xufeng; Liu, Yongshou; Zhang, Yishang; Yue, Zhufeng: Hybrid reliability analysis with both random and probability-box variables (2015)
- Vanhatalo, Jarno; Riihimäki, Jaakko; Hartikainen, Jouni; Jylänki, Pasi; Tolvanen, Ville; Vehtari, Aki: GPstuff: Bayesian modeling with Gaussian processes (2013)
- Park, Chiwoo; Huang, Jianhua Z.; Ding, Yu: GPLP: a local and parallel computation toolbox for Gaussian process regression (2012)