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2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop
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. Thedoi:10.1109/dsp.2009.4785930 fatcat:327sqhpw7nd77clanxgyh6vvsm