Gaussian regression based on models with two stochastic processes

W.E. Leithead, K.S. Neo, D.J. Leith

Research output: Contribution to conferencePaper


When data contains components with different characteristics and it is required to identify both, standard Gaussian regression, based on a model with a single stochastic process, is inadequate. In this paper, a novel adaptation of Gaussian regression, based on models with two stochastic processes, is presented. In both the prior and posterior joint probability distributions, the Gaussian processes for the two components are independent. The effectiveness of the revised Gaussian regression method is demonstrated by application to wind turbine time series data.
Original languageEnglish
Publication statusPublished - 2005
Event16th IFAC World Congress Conference - Prague, Czech Republic
Duration: 4 Jul 20058 Jul 2005


Conference16th IFAC World Congress Conference
Country/TerritoryCzech Republic


  • gaussian regression
  • models
  • stochastic processes
  • identification
  • independent priors
  • gaussian processes
  • independent posteriors


Dive into the research topics of 'Gaussian regression based on models with two stochastic processes'. Together they form a unique fingerprint.

Cite this