{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/bayesian-modeling-with-gaussian-processes","title":"Bayesian Modeling with Gaussian Processes using the GPstuff Toolbox","arxiv_id":"1206.5754","date":"2012-06-25","proceeding":null,"authors":["Jarno Vanhatalo","Jaakko Riihimäki","Jouni Hartikainen","Pasi Jylänki","Ville Tolvanen","Aki Vehtari"],"abstract":"Gaussian processes (GP) are powerful tools for probabilistic modeling\npurposes. They can be used to define prior distributions over latent functions\nin hierarchical Bayesian models. The prior over functions is defined implicitly\nby the mean and covariance function, which determine the smoothness and\nvariability of the function. The inference can then be conducted directly in\nthe function space by evaluating or approximating the posterior process.\nDespite their attractive theoretical properties GPs provide practical\nchallenges in their implementation. GPstuff is a versatile collection of\ncomputational tools for GP models compatible with Linux and Windows MATLAB and\nOctave. It includes, among others, various inference methods, sparse\napproximations and tools for model assessment. In this work, we review these\ntools and demonstrate the use of GPstuff in several models.","url_abs":"http://arxiv.org/abs/1206.5754v6","url_pdf":"http://arxiv.org/pdf/1206.5754v6.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"bayesian-modeling-with-gaussian-processes","repo_url":"https://github.com/gpstuff-dev/gpstuff","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"gaussian-processes","task_name":"Gaussian Processes"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}