Bayesian non parametric inference of discrete valued networks

Laetitia Nouedoui; Pierre Latouche

Bayesian non parametric inference of discrete valued networks

Laetitia Nouedoui
, Pierre Latouche

Université Paris 1 Panthéon-Sorbonne

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

We present a non parametric bayesian inference strategy to automatically infer the number of classes during the clustering process of a discrete valued random network. Our methodology is related to the Dirichlet process mixture models and inference is performed using a Blocked Gibbs sampling procedure. Using simulated data, we show that our approach improves over competitive variational inference clustering methods.

Original language	English
Title of host publication	ESANN 2013 proceedings, 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Publisher	i6doc.com publication
Pages	291-296
Number of pages	6
ISBN (Print)	9782874190810
Publication status	Published - 1 Jan 2013
Externally published	Yes
Event	21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2013 - Bruges, Belgium Duration: 24 Apr 2013 → 26 Apr 2013

Publication series

Name	ESANN 2013 proceedings, 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

Conference

Conference	21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2013
Country/Territory	Belgium
City	Bruges
Period	24/04/13 → 26/04/13

Cite this

Nouedoui, L., & Latouche, P. (2013). Bayesian non parametric inference of discrete valued networks. In ESANN 2013 proceedings, 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (pp. 291-296). (ESANN 2013 proceedings, 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning). i6doc.com publication.

Nouedoui, Laetitia ; Latouche, Pierre. / Bayesian non parametric inference of discrete valued networks. ESANN 2013 proceedings, 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. i6doc.com publication, 2013. pp. 291-296 (ESANN 2013 proceedings, 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning).

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title = "Bayesian non parametric inference of discrete valued networks",

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booktitle = "ESANN 2013 proceedings, 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning",

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Nouedoui, L & Latouche, P 2013, Bayesian non parametric inference of discrete valued networks. in ESANN 2013 proceedings, 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. ESANN 2013 proceedings, 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, i6doc.com publication, pp. 291-296, 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2013, Bruges, Belgium, 24/04/13.

Bayesian non parametric inference of discrete valued networks. / Nouedoui, Laetitia; Latouche, Pierre.
ESANN 2013 proceedings, 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. i6doc.com publication, 2013. p. 291-296 (ESANN 2013 proceedings, 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - We present a non parametric bayesian inference strategy to automatically infer the number of classes during the clustering process of a discrete valued random network. Our methodology is related to the Dirichlet process mixture models and inference is performed using a Blocked Gibbs sampling procedure. Using simulated data, we show that our approach improves over competitive variational inference clustering methods.

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Nouedoui L, Latouche P. Bayesian non parametric inference of discrete valued networks. In ESANN 2013 proceedings, 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. i6doc.com publication. 2013. p. 291-296. (ESANN 2013 proceedings, 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning).

Bayesian non parametric inference of discrete valued networks

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