Detection of abrupt changes: theory and application
Detection of abrupt changes: theory and application
Computational Statistics & Data Analysis
Joint segmentation of the wind speed and direction
Signal Processing
A MAP solution to off-line segmentation of signals
ICASSP '94 Proceedings of the Acoustics, Speech, and Signal Processing,1994. on IEEE International Conference - Volume 04
IEEE Transactions on Signal Processing
Optimal segmentation of random processes
IEEE Transactions on Signal Processing
Bayesian curve fitting using MCMC with applications to signalsegmentation
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
An MCMC sampling approach to estimation of nonstationary hiddenMarkov models
IEEE Transactions on Signal Processing
Exact Bayesian curve fitting and signal segmentation
IEEE Transactions on Signal Processing
Hierarchical Bayesian sparse image reconstruction with application to MRFM
IEEE Transactions on Image Processing
Joint segmentation of multivariate Gaussian processes using mixed linear models
Computational Statistics & Data Analysis
Efficient Bayesian analysis of multiple changepoint models with dependence across segments
Statistics and Computing
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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.