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Title:

Online Gaussian Process regression with non-Gaussian likelihood

Document type:
Konferenzbeitrag
Author(s):
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...     »
Book / Congress title:
The 2017 American Control Conference (ACC)
Year:
2017
Pages:
3134--3140
Covered by:
Scopus; Web of Science
Fulltext / DOI:
doi:10.23919/ACC.2017.7963429
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