By Topic

A Bayesian approach for simultaneous segmentation and classification of count data

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

1 Author(s)
Cappe, O. ; ENST Dept., TSI/CNRS, Paris, France

A Bayesian approach is proposed that provides a concise description of a series of counts under the form of homogeneous consecutive data segments that are classified based on their marginal distribution. Due to the flexibility of the corresponding model, carrying out the actual inference turns out to be a complex task for which an original combination of several Markov chain Monte Carlo (MCMC) simulation tools is developed. The proposed MCMC sampler makes use of reversible jump moves to achieve communication between models with different numbers of both segments and classes. A large section of the paper is devoted to the discussion of the results obtained on a medium-duration section (a few minutes) of a publicly available teletraffic trace taken from the Internet traffic archive

Published in:

Signal Processing, IEEE Transactions on  (Volume:50 ,  Issue: 2 )