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Searching a multivariate partition space using weighted MAX-SAT
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Liverani, Silvia, Cussens, James and Smith, J. Q. (2009) Searching a multivariate partition space using weighted MAX-SAT. UNSPECIFIED. Coventry: University of Warwick. Centre for Research in Statistical Methodology. Working papers, Vol.2009 (No.18).
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Official URL: http://www2.warwick.ac.uk/fac/sci/statistics/crism...
Abstract
Because of the huge number of partitions of even
a moderately sized dataset, even when Bayes factors
have a closed form, a comprehensive search
for the highest scoring (MAP) partition is usually
impossible. Therefore, both deterministic or random
search algorithms traverse only a small proportion
of partitions of a large dataset. The main
contribution of this paper is to encode the formal
Bayes factor search on partitions as a weighted
MAX-SAT problem and use well-known solvers
for that problem to search for partitions. We
demonstrate how, with the appropriate priors
over the partition space, this method can be used
to fully search the space of partitions in smaller
problems and how it can be used to enhance the
performance of more familiar algorithms in large
problems. We illustrate our method on clustering
of time-course microarray experiments.
Item Type: | Working or Discussion Paper (UNSPECIFIED) | ||||
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Subjects: | Q Science > QA Mathematics | ||||
Divisions: | Faculty of Science, Engineering and Medicine > Science > Statistics | ||||
Library of Congress Subject Headings (LCSH): | Partitions (Mathematics), Bayesian statistical decision theory | ||||
Series Name: | Working papers | ||||
Publisher: | University of Warwick. Centre for Research in Statistical Methodology | ||||
Place of Publication: | Coventry | ||||
Official Date: | 2009 | ||||
Dates: |
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Volume: | Vol.2009 | ||||
Number: | No.18 | ||||
Number of Pages: | 8 | ||||
Status: | Not Peer Reviewed | ||||
Access rights to Published version: | Open Access (Creative Commons) | ||||
Date of first compliant deposit: | 1 August 2016 | ||||
Date of first compliant Open Access: | 1 August 2016 |
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