Bayesian evidence and model selection.

Knuth, K. H.; Habeck, M.; Malakar, N. K.; Mubeen, A. M.; Placek, B.

doi:10.1016/j.dsp.2015.06.012

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Bayesian evidence and model selection.

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Habeck, M.
Research Group of Statistical Inverse-Problems in Biophysics, MPI for Biophysical Chemistry, Max Planck Society;

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http://www.sciencedirect.com/science/article/pii/S1051200415001980
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Citation

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.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0029-43AA-9

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.