Benutzer: Gast  Login
Titel:

Online Gaussian Process regression with non-Gaussian likelihood

Dokumenttyp:
Konferenzbeitrag
Autor(en):
Seiferth, David; Chowdhary, Girish; Mühlegg, Maximilian; Holzapfel, Florian
Abstract:
We present a new algorithm for GP regression over data with non-Gaussian likelihood that does not require costly MCMC sampling, or variational Bayes optimization. In our method, which we term Meta-GP, we model the likelihood by another Gaussian Process point-wise in time. This approach allows for the calculation of the posterior predictive mean and variance in an analytical way pointwise in time, leading to an online inference algorithm. As a result, our method can work with streaming data, is a...     »
Kongress- / Buchtitel:
The 2017 American Control Conference (ACC)
Jahr:
2017
Seiten:
3134--3140
Nachgewiesen in:
Scopus; Web of Science
Volltext / DOI:
doi:10.23919/ACC.2017.7963429
 BibTeX