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Information criteria for astrophysical model selection

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posted on 2023-06-08, 05:42 authored by Andrew R Liddle
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 Wilkinson Microwave Anisotropy Probe 3-yr data for several cosmological models. I find that at present the information theory and Bayesian approaches give significantly different conclusions from that data.

History

Publication status

  • Published

File Version

  • Published version

Journal

Monthly Notices of the Royal Astronomical Society

ISSN

0035-8711

Publisher

Wiley-Blackwell

Issue

1

Volume

377

Page range

L74-L78

Department affiliated with

  • Physics and Astronomy Publications

Full text available

  • Yes

Peer reviewed?

  • Yes

Legacy Posted Date

2012-02-06

First Open Access (FOA) Date

2016-03-22

First Compliant Deposit (FCD) Date

2016-11-10

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