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2007

Joint segmentation of wind speed and direction using a hierarchical model

11 years 6 months ago
Joint segmentation of wind speed and direction using a hierarchical model
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. Key words: Change-points, Gibbs sampler, wind data.
Nicolas Dobigeon, Jean-Yves Tourneret
Added 13 Dec 2010
Updated 13 Dec 2010
Type Journal
Year 2007
Where CSDA
Authors Nicolas Dobigeon, Jean-Yves Tourneret
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