By Topic

A Bayesian fusion approach to change-points analysis of processes

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Reboul, S. ; Lab. d''Analyse des Syst. du Littoral, Univ. du Littoral Cote d''Opale, Calais, France ; Benjelloun, M.

We present in this article a Bayesian estimation method for the fusion of change-points detection in a set of piecewise stationary processes. The estimate we propose is based on the maximization of the posterior distribution of the change instants conditionally to the process parameter estimation. It is defined as a penalized contrast function with a first term related to the fit to the observation and a second term of penalty. In the case of joint segmentation, the term of penalty is deduced from the prior law of the change instants. It is composed of parameters that guide the number and the position of changes and parameters that will bring prior information on the joint behavior of processes. We present the construction of the estimator for the fusion detection of changes in the mean and variance of the wind vector. The feasibility and the contribution of our method are shown on experimentations.

Published in:

Information Fusion, 2005 8th International Conference on  (Volume:1 )

Date of Conference:

25-28 July 2005