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Derivative observations in Gaussian process models of dynamic systems

Solak, E. and Murray-Smith, R. and Leithead, W.E. and Rasmusson, C. and Leith, D.J. (2002) Derivative observations in Gaussian process models of dynamic systems. In: 2002 Neural Information Processing (NIPS) Meeting, 2002-12-09 - 2002-12-11.

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Abstract

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.