Information criteria for astrophysical model selection

Details Information criteria for astrophysical model selection Information criteria for astrophysical model selection

2007-01-04

arxiv.org/abs/astro-ph/0701113v2

Mon.Not.Roy.Astron.Soc.Lett.377:L74-L78,2007

Astronomy and Astrophysics

Astrophysics

5 pages, no figures. Update to match version accepted by MNRAS Letters. Extra references, minor changes to discussion, no chan

Scientific paper

10.1111/j.1745-3933.2007.00306.x

Model selection is the problem of distinguishing competing models, perhaps featuring different numbers of parameters. The statistics literature contains two distinct sets of tools, those based on information theory such as the Akaike Information Criterion (AIC), and those on Bayesian inference such as the Bayesian evidence and Bayesian Information Criterion (BIC). The Deviance Information Criterion combines ideas from both heritages; it is readily computed from Monte Carlo posterior samples and, unlike the AIC and BIC, allows for parameter degeneracy. I describe the properties of the information criteria, and as an example compute them from WMAP3 data for several cosmological models. I find that at present the information theory and Bayesian approaches give significantly different conclusions from that data.

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