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  Sparse Linear Models: Bayesian Inference and Experimental Design

Seeger, M. (2008). Sparse Linear Models: Bayesian Inference and Experimental Design. Talk presented at 25th International Conference on Machine Learning (ICML 2008). Helsinki, Finland. 2008-07-05 - 2008-07-09.

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 Creators:
Seeger, M1, 2, Author              
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: Sparse linear models are cornerstones of applied statistics, embodying fundamental ideas such as feature selection, shrinkage, and automatic relevance determination. While much progress has been made recently in understanding point estimation of sparse signals, Bayesian inference is needed to drive higher-level tasks such as experimental design, where valid un- certainties and covariances are more important than point estimates. In this tutorial, the ma- jor determnistic inference approximations to date (expectation propagation, sparse Bayesian learning, variational mean field Bayes) will be introduced for the sparse linear model, and their mathematics (scale mixtures, convex duality, moment matching) will be clarified. Se- quential Bayesian design, with the application to optimizing an image measurement archi- tecture, serves as motivation for this effort.

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 Dates: 2008-07
 Publication Status: Published online
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Title: 25th International Conference on Machine Learning (ICML 2008)
Place of Event: Helsinki, Finland
Start-/End Date: 2008-07-05 - 2008-07-09

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Title: ICML 2008: 25th International Conference on Machine Learning
Source Genre: Proceedings
 Creator(s):
McCallum , A, Editor
Roweis, S, Editor
Affiliations:
-
Publ. Info: Madison, WI, USA : Oregon State University
Pages: - Volume / Issue: - Sequence Number: T9 Start / End Page: XL Identifier: ISBN: 978-1-60558-205-4