Publication detail

Bayesian change detection in the growing window recursive strategy

DOKOUPIL, J. VÁCLAVEK, P.

Original Title

Bayesian change detection in the growing window recursive strategy

Type

conference paper

Language

English

Original Abstract

A novel growing-window recursive algorithm for stochastic system change detection is derived based on the Bayesian inference principle. Model based detectors can be formalized by two concepts in literature: (a) working in a sliding-window strategy because of time-dependent computational complexity, or (b) running in parallel, each one matched to a certain assumption on a change point. This motivates us to investigate a more refined approach which utilizes all relevant data to catch the next change point. The basic idea is to formulate a distance measure between two probabilities, one confirming the change occurrence and the other confirming no change in the system behavior. This study aims to solve the difficulty of sliding time arguments in the compared probabilities as new data are sequentially obtained. The outcome of this analysis is an algorithm that recognizes the time and magnitude of the change occurrence.

Keywords

Bayesian inference, change point probabilities, multiple change points

Authors

DOKOUPIL, J.; VÁCLAVEK, P.

RIV year

2015

Released

21. 7. 2015

Publisher

Trans Tech Publications Ltd

Location

Německo

ISBN

978-3-03835-499-4

Book

Applied mechanics and materials

ISBN

1660-9336

Periodical

Applied Mechanics and Materials

Year of study

775

State

Swiss Confederation

Pages from

399

Pages to

403

Pages count

4

URL

http://www.scientific.net/AMM.775.399

BibTex

@inproceedings{BUT117762,
  author="Jakub {Dokoupil} and Pavel {Václavek}",
  title="Bayesian change detection in the growing window recursive strategy",
  booktitle="Applied mechanics and materials",
  year="2015",
  journal="Applied Mechanics and Materials",
  volume="775",
  pages="399--403",
  publisher="Trans Tech Publications Ltd",
  address="Německo",
  doi="10.1063/1.4912383",
  isbn="978-3-03835-499-4",
  issn="1660-9336",
  url="http://www.scientific.net/AMM.775.399"
}