Derivative observations in Gaussian process models of dynamic systems

E. Solak, R. Murray-Smith, W.E. Leithead, C. Rasmusson, D.J. Leith

Research output: Contribution to conferencePaper


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.

Original languageEnglish
Number of pages8
Publication statusPublished - 2002
Event2002 Neural Information Processing (NIPS) Meeting - Vancouver, Canada
Duration: 9 Dec 200211 Dec 2002


Conference2002 Neural Information Processing (NIPS) Meeting


  • derivative observations
  • gaussian process models
  • dynamic systems


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