{"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/chained-gaussian-processes","title":"Chained Gaussian Processes","arxiv_id":"1604.05263","date":"2016-04-18","proceeding":null,"authors":["Alan D. Saul","James Hensman","Aki Vehtari","Neil D. Lawrence"],"abstract":"Gaussian process models are flexible, Bayesian non-parametric approaches to\nregression. Properties of multivariate Gaussians mean that they can be combined\nlinearly in the manner of additive models and via a link function (like in\ngeneralized linear models) to handle non-Gaussian data. However, the link\nfunction formalism is restrictive, link functions are always invertible and\nmust convert a parameter of interest to a linear combination of the underlying\nprocesses. There are many likelihoods and models where a non-linear combination\nis more appropriate. We term these more general models Chained Gaussian\nProcesses: the transformation of the GPs to the likelihood parameters will not\ngenerally be invertible, and that implies that linearisation would only be\npossible with multiple (localized) links, i.e. a chain. We develop an\napproximate inference procedure for Chained GPs that is scalable and applicable\nto any factorized likelihood. We demonstrate the approximation on a range of\nlikelihood functions.","url_abs":"http://arxiv.org/abs/1604.05263v1","url_pdf":"http://arxiv.org/pdf/1604.05263v1.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":"chained-gaussian-processes","repo_url":"https://github.com/SheffieldML/ChainedGP","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"additive-models","task_name":"Additive models"},{"task_slug":"gaussian-processes","task_name":"Gaussian Processes"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1604.05263","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}