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  Bayesian evidence and model selection.

Knuth, K. H., Habeck, M., Malakar, N. K., Mubeen, A. M., & Placek, B. (2015). Bayesian evidence and model selection. Digital Signal Processing, 47, 50-67. doi:10.1016/j.dsp.2015.06.012.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0029-43AA-9 Version Permalink: http://hdl.handle.net/11858/00-001M-0000-002A-1658-A
Genre: Journal Article

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2240283.pdf (Publisher version), 2MB
 
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 Creators:
Knuth, K. H., Author
Habeck, M.1, Author              
Malakar, N. K., Author
Mubeen, A. M., Author
Placek, B., Author
Affiliations:
1Research Group of Statistical Inverse-Problems in Biophysics, MPI for Biophysical Chemistry, Max Planck Society, ou_1113580              

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Free keywords: Bayesian signal processing; Bayesian evidence; Model testing; Nested sampling; Odds ratio
 Abstract: In this paper we review the concepts of Bayesian evidence and Bayes factors, also known as log odds ratios, and their application to model selection. The theory is presented along with a discussion of analytic, approximate and numerical techniques. Specific attention is paid to the Laplace approximation, variational Bayes, importance sampling, thermodynamic integration, and nested sampling and its recent variants. Analogies to statistical physics, from which many of these techniques originate, are discussed in order to provide readers with deeper insights that may lead to new techniques. The utility of Bayesian model testing in the domain sciences is demonstrated by presenting four specific practical examples considered within the context of signal processing in the areas of signal detection, sensor characterization, scientific model selection and molecular force characterization.

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Language(s): eng - English
 Dates: 2015-12
 Publication Status: Published in print
 Pages: -
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 Table of Contents: -
 Rev. Method: Peer
 Identifiers: DOI: 10.1016/j.dsp.2015.06.012
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Title: Digital Signal Processing
Source Genre: Journal
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Pages: - Volume / Issue: 47 Sequence Number: - Start / End Page: 50 - 67 Identifier: -