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  Derivative observations in Gaussian Process models of dynamic systems

Solak, E., Murray-Smith, R., Leithead, W., Leith, D., & Rasmussen, C. (2003). Derivative observations in Gaussian Process models of dynamic systems. In S. Becker, S. Thrun, & K. Obermayer (Eds.), Advances in Neural Information Processing Systems 15 (pp. 1033-1040). Cambridge, MA, USA: MIT Press.

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 Urheber:
Solak, E, Autor
Murray-Smith , R, Autor
Leithead , WE, Autor
Leith, D, Autor
Rasmussen, CE1, Autor           
Affiliations:
1External Organizations, ou_persistent22              

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Schlagwörter: -
 Zusammenfassung: Gaussian processes provide an approach to nonparametric modelling which allows a straightforward combination of function and derivative observations in an empirical model. This is of particular importance in identification of nonlinear dynamic systems from experimental data. 1) It
allows us to combine derivative information, and associated uncertainty with normal function observations into the learning and inference process. This derivative information can be in the form of priors specified by an expert or identified from perturbation data close to equilibrium. 2)
It allows a seamless fusion of multiple local linear models in a consistent manner, inferring consistent models and ensuring that integrability constraints are met. 3) It improves dramatically the computational efficiency
of Gaussian process models for dynamic system identification, by summarising large quantities of near-equilibrium data by a handful of linearisations, reducing the training set size - traditionally a problem for
Gaussian process models.

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 Datum: 2003-09
 Publikationsstatus: Erschienen
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: -
 Identifikatoren: BibTex Citekey: 2107
 Art des Abschluß: -

Veranstaltung

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Titel: Sixteenth Annual Conference on Neural Information Processing Systems (NIPS 2002)
Veranstaltungsort: Vancouver, BC, Canada
Start-/Enddatum: 2002-12-09 - 2002-12-14

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Titel: Advances in Neural Information Processing Systems 15
Genre der Quelle: Konferenzband
 Urheber:
Becker, S, Herausgeber
Thrun, S, Herausgeber
Obermayer, K, Herausgeber
Affiliations:
-
Ort, Verlag, Ausgabe: Cambridge, MA, USA : MIT Press
Seiten: - Band / Heft: - Artikelnummer: - Start- / Endseite: 1033 - 1040 Identifikator: ISBN: 0-262-02550-7