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Gaussian regression based on models with two stochastic processes

Leithead, W.E. and Neo, K.S. and Leith, D.J. (2005) Gaussian regression based on models with two stochastic processes. In: 16th IFAC World Congress Conference, 2005-07-04 - 2005-07-08, Prague.

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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.

Item type: Conference or Workshop Item (Paper)
ID code: 37988
Keywords: gaussian regression, models, stochastic processes, identification, independent priors, gaussian processes, independent posteriors, Electrical engineering. Electronics Nuclear engineering
Subjects: Technology > Electrical engineering. Electronics Nuclear engineering
Department: Faculty of Engineering > Electronic and Electrical Engineering
Related URLs:
    Depositing user: Pure Administrator
    Date Deposited: 28 Feb 2012 16:19
    Last modified: 06 Sep 2014 15:40
    URI: http://strathprints.strath.ac.uk/id/eprint/37988

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