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High-dimensional Bayesian classifiers using non-local priors
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Rossell, David, Telesca, Donatello and Johnson, Valen E. (2013) High-dimensional Bayesian classifiers using non-local priors. In: Giudici, Paolo and Ingrassia, Salvatore and Vichi, Maurizio, (eds.) Statistical Models for Data Analysis: Studies in Classification, Data Analysis, and Knowledge Organization. Springer International Publishing, pp. 305-313. ISBN 9783319000312
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Official URL: http://dx.doi.org/10.1007/978-3-319-00032-9_35
Abstract
Common goals in classification problems are (i) obtaining predictions and (ii) identifying subsets of highly predictive variables. Bayesian classifiers quantify the uncertainty in all steps of the prediction. However, common Bayesian procedures can be slow in excluding features with no predictive power (Johnson & Rossell. (2010). In certain high-dimensional setups the posterior probability assigned to the correct set of predictors converges to 0 (Johnson and Rossell 2012). We study the use of non-local priors (NLP), which overcome the above mentioned limitations. We introduce a new family of NLP and derive efficient MCMC schemes.
Item Type: | Book Item | ||||
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Statistics | ||||
Journal or Publication Title: | Statistical Models for Data Analysis | ||||
Publisher: | Springer International Publishing | ||||
ISBN: | 9783319000312 | ||||
ISSN: | 1431-8814 | ||||
Book Title: | Statistical Models for Data Analysis: Studies in Classification, Data Analysis, and Knowledge Organization | ||||
Editor: | Giudici, Paolo and Ingrassia, Salvatore and Vichi, Maurizio | ||||
Official Date: | 22 May 2013 | ||||
Dates: |
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Page Range: | pp. 305-313 | ||||
DOI: | 10.1007/978-3-319-00032-9-35 | ||||
Status: | Peer Reviewed | ||||
Publication Status: | Published | ||||
Access rights to Published version: | Restricted or Subscription Access |
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