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Asymptotically Exact Data Augmentation: ...
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Document type :
Article dans une revue scientifique
DOI :
10.1080/10618600.2020.1826954
Title :
Asymptotically Exact Data Augmentation: Models, Properties, and Algorithms
Author(s) :
Vono, Maxime [Auteur]
Signal et Communications [IRIT-SC]
Dobigeon, Nicolas [Auteur]
Institut National Polytechnique (Toulouse) [Toulouse INP]
Institut Universitaire de France [IUF]
Signal et Communications [IRIT-SC]
Chainais, Pierre [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Journal title :
Journal of Computational and Graphical Statistics
Pages :
335-348
Publisher :
Taylor & Francis
Publication date :
2021
ISSN :
1061-8600
English keyword(s) :
Approximation
Auxiliary variables
Bayesian inference
Divide-and-conquer
Robustness
HAL domain(s) :
Informatique [cs]/Intelligence artificielle [cs.AI]
Informatique [cs]/Vision par ordinateur et reconnaissance de formes [cs.CV]
Physique [physics]/Physique [physics]/Analyse de données, Statistiques et Probabilités [physics.data-an]
Informatique [cs]/Apprentissage [cs.LG]
Informatique [cs]/Traitement des images [eess.IV]
Informatique [cs]/Traitement du signal et de l'image [eess.SP]
English abstract : [en]
Data augmentation, by the introduction of auxiliary variables, has become an ubiquitous technique to improve convergence properties, simplify the implementation or reduce the computational time of inference methods such ...
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Data augmentation, by the introduction of auxiliary variables, has become an ubiquitous technique to improve convergence properties, simplify the implementation or reduce the computational time of inference methods such as Markov chain Monte Carlo ones. Nonetheless, introducing appropriate auxiliary variables while preserving the initial target probability distribution and offering a computationally efficient inference cannot be conducted in a systematic way. To deal with such issues, this article studies a unified framework, coined asymptotically exact data augmentation (AXDA), which encompasses both well-established and more recent approximate augmented models. In a broader perspective, this article shows that AXDA models can benefit from interesting statistical properties and yield efficient inference algorithms. In non-asymptotic settings, the quality of the proposed approximation is assessed with several theoretical results. The latter are illustrated on standard statistical problems.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
ANR Project :
Artificial and Natural Intelligence Toulouse Institute
Collections :
  • Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
Source :
Harvested from HAL
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  • http://arxiv.org/pdf/1902.05754
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