Restarted Bayesian Online Change-point ...
Type de document :
Communication dans un congrès avec actes
Titre :
Restarted Bayesian Online Change-point Detector achieves Optimal Detection Delay
Auteur(s) :
Alami, Réda [Auteur]
Orange Labs [Lannion]
Maillard, Odalric Ambrym [Auteur]
Scool [Scool]
Féraud, Raphael [Auteur]
Orange Labs [Lannion]
Orange Labs [Lannion]
Maillard, Odalric Ambrym [Auteur]
Scool [Scool]
Féraud, Raphael [Auteur]
Orange Labs [Lannion]
Titre de la manifestation scientifique :
International Conference on Machine Learning
Ville :
Wien
Pays :
Autriche
Date de début de la manifestation scientifique :
2020-07
Date de publication :
2020-07
Discipline(s) HAL :
Statistiques [stat]/Machine Learning [stat.ML]
Mathématiques [math]/Statistiques [math.ST]
Mathématiques [math]/Statistiques [math.ST]
Résumé en anglais : [en]
In this paper, we consider the problem of sequential change-point detection where both the changepoints and the distributions before and after the change are assumed to be unknown. For this problem of primary importance ...
Lire la suite >In this paper, we consider the problem of sequential change-point detection where both the changepoints and the distributions before and after the change are assumed to be unknown. For this problem of primary importance in statistical and sequential learning theory, we derive a variant of the Bayesian Online Change Point Detector proposed by (Fearnhead & Liu, 2007) which is easier to analyze than the original version while keeping its powerful message-passing algorithm. We provide a non-asymptotic analysis of the false-alarm rate and the detection delay that matches the existing lower-bound. We further provide the first explicit high-probability control of the detection delay for such approach. Experiments on synthetic and realworld data show that this proposal outperforms the state-of-art change-point detection strategy, namely the Improved Generalized Likelihood Ratio (Improved GLR) while compares favorably with the original Bayesian Online Change Point Detection strategy.Lire moins >
Lire la suite >In this paper, we consider the problem of sequential change-point detection where both the changepoints and the distributions before and after the change are assumed to be unknown. For this problem of primary importance in statistical and sequential learning theory, we derive a variant of the Bayesian Online Change Point Detector proposed by (Fearnhead & Liu, 2007) which is easier to analyze than the original version while keeping its powerful message-passing algorithm. We provide a non-asymptotic analysis of the false-alarm rate and the detection delay that matches the existing lower-bound. We further provide the first explicit high-probability control of the detection delay for such approach. Experiments on synthetic and realworld data show that this proposal outperforms the state-of-art change-point detection strategy, namely the Improved Generalized Likelihood Ratio (Improved GLR) while compares favorably with the original Bayesian Online Change Point Detection strategy.Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Collections :
Source :
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- SupplementaryMaterials_BOCPD_ICML2020.zip
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