MCMC Sampling for Joint Segmentation of Wind Speed and Direction

Nicolas Dobigeon, Jean-Yves Tourneret
2009 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. The
more » ... meters. The performance of the proposed algorithm is illustrated with results obtained with synthetic data. Index Terms-Monte Carlo methods, Bayesian inference, hierarchical model, joint segmentation, wind data.
doi:10.1109/dsp.2009.4785930 fatcat:327sqhpw7nd77clanxgyh6vvsm