Properties of variational estimates of a ...
Type de document :
Communication dans un congrès avec actes
Titre :
Properties of variational estimates of a mixture model for random graphs
Auteur(s) :
Daudin, Jean-Jacques [Auteur]
Mathématiques et Informatique Appliquées [MIA-Paris]
Célisse, Alain [Auteur]
Mathématiques et Informatique Appliquées [MIA-Paris]
Laboratoire Paul Painlevé - UMR 8524 [LPP]
Gazal, Steven [Auteur]
Mathématiques et Informatique Appliquées [MIA-Paris]
Robin, Stephane [Auteur]
Mathématiques et Informatique Appliquées [MIA-Paris]
Mathématiques et Informatique Appliquées [MIA-Paris]
Célisse, Alain [Auteur]
Mathématiques et Informatique Appliquées [MIA-Paris]
Laboratoire Paul Painlevé - UMR 8524 [LPP]
Gazal, Steven [Auteur]
Mathématiques et Informatique Appliquées [MIA-Paris]
Robin, Stephane [Auteur]
Mathématiques et Informatique Appliquées [MIA-Paris]
Titre de la manifestation scientifique :
ECCS10 European Conference on Complex Systems
Ville :
Lisbonne
Pays :
France
Date de début de la manifestation scientifique :
2010-09-13
Titre de l’ouvrage :
European Conference on Complex Systems 10: programme and Abstracts
Titre de la revue :
European Conference on Complex Systems 10: programme and Abstracts
Date de publication :
2010
Mot(s)-clé(s) en anglais :
biological network
mixture model
random graph model
variational inference variational Bayes inference
mixture model
random graph model
variational inference variational Bayes inference
Discipline(s) HAL :
Sciences du Vivant [q-bio]
Résumé en anglais : [en]
Mixture models for random graphs have a complex depen- dency structure and a likelihood which is not computable even for mod- erate size networks. Variational and variational Bayes techniques are useful approaches for ...
Lire la suite >Mixture models for random graphs have a complex depen- dency structure and a likelihood which is not computable even for mod- erate size networks. Variational and variational Bayes techniques are useful approaches for statistical inference of such complex models but their theoretical properties are not well known. We give a result about the consistency of variational estimates of the parameters of the model and we propose variational Bayes estimates. We compare the accuracy of the two variational methods through simulation studies and show an application to a large Protein-Protein interaction network.Lire moins >
Lire la suite >Mixture models for random graphs have a complex depen- dency structure and a likelihood which is not computable even for mod- erate size networks. Variational and variational Bayes techniques are useful approaches for statistical inference of such complex models but their theoretical properties are not well known. We give a result about the consistency of variational estimates of the parameters of the model and we propose variational Bayes estimates. We compare the accuracy of the two variational methods through simulation studies and show an application to a large Protein-Protein interaction network.Lire moins >
Langue :
Anglais
Comité de lecture :
Non
Audience :
Non spécifiée
Vulgarisation :
Non
Commentaire :
Satellite meeting<br/>Satellite meeting
Collections :
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