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Scalable Bayesian nonparametric measures for exploring pairwise dependence via Dirichlet Process Mixtures

Abstract:

In this article we propose novel Bayesian nonparametric methods using Dirichlet Process Mixture (DPM) models for detecting pairwise dependence between random variables while accounting for uncertainty in the form of the underlying distributions. A key criteria is that the procedures should scale to large data sets. In this regard we find that the formal calculation of the Bayes factor for a dependent-vs.-independent DPM joint probability measure is not feasible computationally. To address thi...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1214/16-EJS1171

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Institution:
University of Oxford
Division:
MPLS
Department:
Statistics
Role:
Author
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Institution:
University of Oxford
Division:
MPLS
Department:
Statistics
Role:
Author
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Funding agency for:
Holmes, C
Grant:
MC UP A390 1107
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Funding agency for:
Holmes, C
Grant:
MC UP A390 1107
More from this funder
Funding agency for:
Holmes, C
Grant:
MC UP A390 1107
More from this funder
Funding agency for:
Holmes, C
Grant:
MC UP A390 1107
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Funding agency for:
Nieto Barajas, L
Grant:
244459
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Publisher:
Institute of Mathematical Statistics Publisher's website
Journal:
Electronic Journal of Statistics Journal website
Volume:
10
Issue:
2
Pages:
3338-3354
Publication date:
2016-11-16
Acceptance date:
2016-07-30
DOI:
ISSN:
1935-7524
Source identifiers:
641240
Keywords:
Pubs id:
pubs:641240
UUID:
uuid:d9c1b009-e2ff-4217-8be4-ab715fc7cb91
Local pid:
pubs:641240
Deposit date:
2016-09-02

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