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Characterizing variation of nonparametric random probability measures using the Kullback–Leibler divergence

Abstract:
This work characterizes the dispersion of some popular random probability measures, including the bootstrap, the Bayesian bootstrap, and the Pólya tree prior. This dispersion is measured in terms of the variation of the Kullback–Leibler divergence of a random draw from the process to that of its baseline centring measure. By providing a quantitative expression of this dispersion around the baseline distribution, our work provides insight for comparing different parameterizations of the models and for the setting of prior parameters in applied Bayesian settings. This highlights some limitations of the existing canonical choice of parameter settings in the Pólya tree process.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1080/02331888.2016.1258072

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Institution:
University of Oxford
Division:
MSD
Department:
NDM
Sub department:
Human Genetics Wt Centre
Role:
Author
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Institution:
University of Oxford
Oxford college:
St Anne's College
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
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Funding agency for:
Nieto-Barajas, L
Grant:
244459
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Funding agency for:
Nieto-Barajas, L
Grant:
244459
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Funding agency for:
Watson, J


Publisher:
Taylor and Francis
Journal:
Statistics More from this journal
Volume:
51
Issue:
3
Pages:
558-571
Publication date:
2016-11-16
Acceptance date:
2016-07-20
DOI:
EISSN:
1029-4910
ISSN:
0233-1888


Keywords:
Pubs id:
pubs:664450
UUID:
uuid:48f47373-28e0-49e8-9729-1251b764bd7a
Local pid:
pubs:664450
Source identifiers:
664450
Deposit date:
2017-03-02

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