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Joint segmentation of wind speed and direction using a hierarchical model

Dobigeon, Nicolas and Tourneret, Jean-Yves Joint segmentation of wind speed and direction using a hierarchical model. (2007) Computational Statistics & Data Analysis, vol. 5 (n° 12). pp. 5603-5621. ISSN 0167-9473

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Official URL: http://dx.doi.org/10.1016/j.csda.2007.04.016

Abstract

The problem of detecting changes in wind speed and direction is considered. Bayesian priors, with various degrees of certainty, are used to represent relationships between the two time series. Segmentation is then conducted using a hierarchical Bayesian model that accounts for correlations between the wind speed and direction. A Gibbs sampling strategy overcomes the computational complexity of the hierarchical model and is used to estimate the unknown parameters and hyperparameters. Extensions to other statistical models are also discussed. These models allow us to study other joint segmentation problems including segmentation of wave amplitude and direction. The performance of the proposed algorithms is illustrated with results obtained with synthetic and real data.

Item Type:Article
Additional Information:This publication is available on http://www.sciencedirect.com/science/journal/01679473
Audience (journal):International peer-reviewed journal
Uncontrolled Keywords:
Institution: Université de Toulouse > Institut National Polytechnique de Toulouse - INPT
Université de Toulouse > Université Paul Sabatier-Toulouse III - UPS
French research institutions > Centre National de la Recherche Scientifique - CNRS
Laboratory name:
Statistics:download
Deposited By: Jean-yves TOURNERET

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