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Bayesian prediction via partitioning

Abstract:

This article proposes a new Bayesian approach to prediction on continuous covariates. The Bayesian partition model constructs arbitrarily complex regression and classification surfaces by splitting the covariate space into an unknown number of disjoint regions. Within each region the data are assumed to be exchangeable and come from some simple distribution. Using conjugate priors, the marginal likelihoods of the models can be obtained analytically for any proposed partitioning of the space w...

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Publication status:
Published

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Publisher copy:
10.1198/106186005X78107

Authors


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Institution:
University of Oxford
Division:
MSD
Department:
NDM
Role:
Author
Journal:
JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS More from this journal
Volume:
14
Issue:
4
Pages:
811-830
Publication date:
2005-12-01
DOI:
EISSN:
1537-2715
ISSN:
1061-8600
Language:
English
Keywords:
Pubs id:
pubs:97548
UUID:
uuid:0cd7f6cc-87f9-4a54-b1c1-cf179a1ef7c6
Local pid:
pubs:97548
Source identifiers:
97548
Deposit date:
2012-12-19

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