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On the marginal likelihood and crossvalidation

Abstract:

In Bayesian statistics, the marginal likelihood, also known as the evidence, is used to evaluate model fit as it quantifies the joint probability of the data under the prior. In contrast, non-Bayesian models are typically compared using cross-validation on held-out data, either through k-fold partitioning or leave-p-out subsampling. We show that the marginal likelihood is formally equivalent to exhaustive leave-p-out crossvalidation averaged over all values of p and all held-out test sets whe...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1093/biomet/asz077

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More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Statistics
Oxford college:
Wolfson College
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Statistics
Oxford college:
St Anne's College
Role:
Author
Publisher:
Oxford University Press Publisher's website
Journal:
Biometrika Journal website
Volume:
107
Issue:
2
Pages:
489–496
Publication date:
2020-01-24
Acceptance date:
2019-08-22
DOI:
EISSN:
1464-3510
ISSN:
0006-3444
Language:
English
Keywords:
Pubs id:
1086862
Local pid:
pubs:1086862
Deposit date:
2020-02-11

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