Sparse point estimation for Bayesian regression via simulated annealing

Raman, Sudhir and Roth, Volker. (2012) Sparse point estimation for Bayesian regression via simulated annealing. In: Pattern recognition : joint 34th DAGM and 36th OAGM Symposium. Springer, pp. 317-326.

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Official URL: http://edoc.unibas.ch/dok/A6018452

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In the context of variable selection in a regression model, the classical Lasso based optimization approach provides a sparse estimate with respect to regression coefficients but is unable to provide more information regarding the distribution of regression coefficients. Alternatively, using a Bayesian approach is more advantageous since it gives direct access to the distribution which is usually summarized by estimating the expectation (not sparse) and variance. Additionally, to support frequent application requirements, heuristics like thresholding are generally used to produce sparse estimates for variable selection purposes. In this paper, we provide a more principled approach for generating a sparse point estimate in a Bayesian framework. We extend an existing Bayesian framework for sparse regression to generate a MAP estimate by using simulated annealing. We then justify this extension by showing that this MAP estimate is also sparse in the regression coefficients. Experiments on real world applications like the splice site detection and diabetes progression demonstrate the usefulness of the extension.
Faculties and Departments:05 Faculty of Science > Departement Mathematik und Informatik > Informatik > Datenanalyse (Roth)
UniBasel Contributors:Roth, Volker and Shankar Raman, Sudhir
Item Type:Conference or Workshop Item, refereed
Conference or workshop item Subtype:Conference Paper
Bibsysno:Link to catalogue
Series Name:Lecture Notes in Computer Science
Issue Number:7476
Note:Publication type according to Uni Basel Research Database: Conference paper
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Last Modified:07 Mar 2014 09:55
Deposited On:13 Sep 2013 07:58

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