MCMC Sampling for Joint Segmentation of Wind Speed and Direction

Author

Dobigeon, Nicolas ; Tourneret, Jean-Yves

Author_Institution

IRIT/INP-ENSEEIHT, Univ. of Toulouse, Toulouse

fYear

2009

fDate

4-7 Jan. 2009

Firstpage

250

Lastpage

255

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. The performance of the proposed algorithm is illustrated with results obtained with synthetic data.

Keywords

Markov processes; Monte Carlo methods; signal sampling; time series; Bayesian priors; Gibbs sampling strategy; MCMC sampling; hierarchical Bayesian model; hyperparameters; joint segmentation; time series; wind direction; wind speed; Bayesian methods; Change detection algorithms; Computational complexity; Inference algorithms; Least squares methods; Maximum likelihood detection; Parameter estimation; Sampling methods; Terminology; Wind speed; Bayesian inference; Monte Carlo methods; hierarchical model; joint segmentation; wind data;

fLanguage

English

Publisher

ieee

Conference_Titel

Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop, 2009. DSP/SPE 2009. IEEE 13th

Conference_Location

Marco Island, FL

Print_ISBN

978-1-4244-3677-4

Electronic_ISBN

978-1-4244-3677-4

Type

conf

DOI

10.1109/DSP.2009.4785930

Filename

4785930