Chained Gaussian Processes

Loading...
Thumbnail Image
Journal Title
Journal ISSN
Volume Title
Conference article in proceedings
Date
2016
Major/Subject
Mcode
Degree programme
Language
en
Pages
10
1431-1440
Series
Journal of Machine Learning Research: Workshop and Conference Proceedings, Volume 51
Abstract
Gaussian process models are flexible, Bayesian non-parametric approaches to regression. Properties of multivariate Gaussians mean that they can be combined linearly in the manner of additive models and via a link function (like in generalized linear models) to handle non-Gaussian data. However, the link function formalism is restrictive, link functions are always invertible and must convert a parameter of interest to an linear combination of the underlying processes. There are many likelihoods and models where a non-linear combination is more appropriate. We term these more general models “Chained Gaussian Processes”: the transformation of the GPs to the likelihood parameters will not generally be invertible, and that implies that linearisation would only be possible with multiple (localized) links, i.e a chain. We develop an approximate inference procedure for Chained GPs that is scalable and applicable to any factorized likelihood. We demonstrate the approximation on a range of likelihood functions.
Description
Keywords
Other note
Citation
Saul , A , Hensman , J , Vehtari , A & Lawrence , N D 2016 , Chained Gaussian Processes . in Journal of Machine Learning Research: Workshop and Conference Proceedings : AISTATS 2016 Proceedings . vol. 51 , Journal of Machine Learning Research: Workshop and Conference Proceedings , vol. 51 , JMLR , pp. 1431-1440 , International Conference on Artificial Intelligence and Statistics , Cadiz , Spain , 09/05/2016 . < http://jmlr.org/proceedings/papers/v51/saul16.pdf >