Journal article
A Bayesian nonparametric approach to testing for dependence between random variables
- Abstract:
-
Nonparametric and nonlinear measures of statistical dependence between pairs of random variables are important tools in modern data analysis. In particular the emergence of large data sets can now support the relaxation of linearity as- sumptions implicit in traditional association scores such as correlation. Here we describe a Bayesian nonparametric procedure that leads to a tractable, explicit and analytic quantification of the relative evidence for dependence vs independence. Our approach ...
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- Publication status:
- Published
- Peer review status:
- Peer reviewed
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Authors
Funding
+ Engineering and Physical Sciences Research Council
More from this funder
Funding agency for:
Holmes, C
Grant:
EP/K014463/1
Bibliographic Details
- Publisher:
- International Society for Bayesian Analysis Publisher's website
- Journal:
- Bayesian Analysis Journal website
- Volume:
- 12
- Issue:
- 4
- Pages:
- 919-938
- Publication date:
- 2016-09-21
- Acceptance date:
- 2016-09-01
- DOI:
- EISSN:
-
1931-6690
- ISSN:
-
1936-0975
Item Description
- Keywords:
- Pubs id:
-
pubs:641241
- UUID:
-
uuid:cc59269c-4b68-4921-8354-21bc0b58b609
- Local pid:
- pubs:641241
- Source identifiers:
-
641241
- Deposit date:
- 2016-09-02
Terms of use
- Copyright holder:
- © 2015 International Society for Bayesian Analysis
- Copyright date:
- 2016
- Notes:
- This is an Open Access item published under a Creative Commons licence, see: https://creativecommons.org/licenses/by/4.0/
- Licence:
- CC Attribution (CC BY)
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