Bayesian change detection in the growing window recursive strategy

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26620%2F15%3APU115905" target="_blank" >RIV/00216305:26620/15:PU115905 - isvavai.cz</a>

Výsledek na webu

<a href="http://www.scientific.net/AMM.775.399" target="_blank" >http://www.scientific.net/AMM.775.399</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1063/1.4912383" target="_blank" >10.1063/1.4912383</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Bayesian change detection in the growing window recursive strategy

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Bayesian change detection in the growing window recursive strategy

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2015

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Applied mechanics and materials

ISBN

978-3-03835-499-4

ISSN

1660-9336

e-ISSN

—

Počet stran výsledku

4

Strana od-do

399-403

Název nakladatele

Trans Tech Publications Ltd

Místo vydání

Německo

Místo konání akce

Hong Kong

Datum konání akce

19. 4. 2015

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—