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Bayesian cluster analysis : point estimation and credible balls
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Wade, Sara and Ghahramani, Zoubin (2017) Bayesian cluster analysis : point estimation and credible balls. Bayesian Analysis, 13 (2). pp. 559-626. doi:10.1214/17-BA1073 ISSN 1931-6690.
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Official URL: http://doi.org/10.1214/17-BA1073
Abstract
Clustering is widely studied in statistics and machine learning, with applications in a variety of fields. As opposed to popular algorithms such as agglomerative hierarchical clustering or k-means which return a single clustering solution, Bayesian nonparametric models provide a posterior over the entire space of partitions, allowing one to assess statistical properties, such as uncertainty on the number of clusters. However, an important problem is how to summarize the posterior; the huge dimension of partition space and difficulties in visualizing it add to this problem. In a Bayesian analysis, the posterior of a real-valued parameter of interest is often summarized by reporting a point estimate such as the posterior mean along with 95% credible intervals to characterize uncertainty. In this paper, we extend these ideas to develop appropriate point estimates and credible sets to summarize the posterior of the clustering structure based on decision and information theoretic techniques.
Item Type: | Journal Article | ||||||
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Subjects: | Q Science > QA Mathematics Q Science > QC Physics Q Science > QH Natural history |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Statistics | ||||||
Library of Congress Subject Headings (LCSH): | Cluster analysis, Bayesian field theory, Bioinformatics, Markov processes | ||||||
Journal or Publication Title: | Bayesian Analysis | ||||||
Publisher: | International Society for Bayesian Analysis | ||||||
ISSN: | 1931-6690 | ||||||
Official Date: | 19 October 2017 | ||||||
Dates: |
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Volume: | 13 | ||||||
Number: | 2 | ||||||
Page Range: | pp. 559-626 | ||||||
DOI: | 10.1214/17-BA1073 | ||||||
Status: | Peer Reviewed | ||||||
Publication Status: | Published | ||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||
Date of first compliant deposit: | 13 September 2017 | ||||||
Date of first compliant Open Access: | 29 January 2018 | ||||||
RIOXX Funder/Project Grant: |
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