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.
|Number of pages||8|
|Publication status||Published - 2002|
|Event||2002 Neural Information Processing (NIPS) Meeting - Vancouver, Canada|
Duration: 9 Dec 2002 → 11 Dec 2002
|Conference||2002 Neural Information Processing (NIPS) Meeting|
|Period||9/12/02 → 11/12/02|
- derivative observations
- gaussian process models
- dynamic systems
Solak, E., Murray-Smith, R., Leithead, W. E., Rasmusson, C., & Leith, D. J. (2002). Derivative observations in Gaussian process models of dynamic systems. Paper presented at 2002 Neural Information Processing (NIPS) Meeting , Vancouver, Canada.