Journal article
Graphical model selection for Gaussian conditional random fields in the presence of latent variables
- Abstract:
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We consider the problem of learning a conditional Gaussian graphical model in the presence of latent variables. Building on recent advances in this field, we suggest a method that decomposes the parameters of a conditional Markov random field into the sum of a sparse and a low-rank matrix. We derive convergence bounds for this estimator and show that it is well-behaved in the high-dimensional regime as well as “sparsistent” (i.e., capable of recovering the graph structure). We then show how p...
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- Publication status:
- Published
- Peer review status:
- Peer reviewed
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Access Document
- Files:
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(Version of record, pdf, 2.1MB)
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- Publisher copy:
- 10.1080/01621459.2018.1434531
Authors
Funding
+ Wellcome Trust
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Funding agency for:
McVean, G
Grant:
100956/Z/13/Z
100956/Z/13/Z
University of Bristol
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Bibliographic Details
- Publisher:
- Taylor and Francis Publisher's website
- Journal:
- Journal of the American Statistical Association Journal website
- Pages:
- 1-12
- Publication date:
- 2018-07-11
- Acceptance date:
- 2018-02-13
- DOI:
- EISSN:
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1537-274X
- ISSN:
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0162-1459
Item Description
- Keywords:
- Pubs id:
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pubs:906074
- UUID:
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uuid:539e3785-8309-43a5-a360-cc73b04b9907
- Local pid:
- pubs:906074
- Source identifiers:
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906074
- Deposit date:
- 2018-08-15
Terms of use
- Copyright holder:
- Frot et al
- Copyright date:
- 2018
- Notes:
-
© 2018 The Author(s).Published with license
by Taylor & Francis. © Benjamin Frot, Luke Jostins, and Gilean McVean. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
- Licence:
- CC Attribution (CC BY)
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