Gaussian regression based on models with two stochastic processes

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

Research output: Contribution to conferencePaper

Abstract

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
DOIs
Publication statusPublished - 2005
Event16th IFAC World Congress Conference - Prague, Czech Republic
Duration: 4 Jul 20058 Jul 2005

Conference

Conference16th IFAC World Congress Conference
CountryCzech Republic
CityPrague
Period4/07/058/07/05

Keywords

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

Cite this

Leithead, W. E., Neo, K. S., & Leith, D. J. (2005). Gaussian regression based on models with two stochastic processes. Paper presented at 16th IFAC World Congress Conference , Prague, Czech Republic. https://doi.org/10.3182/20050703-6-CZ-1902.00024