User: Guest  Login
Document type:
Konferenzbeitrag 
Author(s):
Seiferth, David; Chowdhary, Girish; Mühlegg, Maximilian; Holzapfel, Florian 
Title:
Online Gaussian Process regression with non-Gaussian likelihood 
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...    »
 
Book / Congress title:
The 2017 American Control Conference (ACC) 
Year:
2017 
Pages:
3134--3140 
Covered by:
Scopus; Web of Science