Journal Article

Model selection in cosmology

Andrew Liddle, Pia Mukherjee and David Parkinson

in Astronomy & Geophysics

Published on behalf of The Royal Astronomical Society

Volume 47, issue 4, pages 4.30-4.33
Published in print August 2006 | ISSN: 1366-8781
Published online August 2006 | e-ISSN: 1468-4004 | DOI:
Model selection in cosmology

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Model selection aims to determine which theoretical models are most plausible given some data, without necessarily considering preferred values of model parameters. A common model selection question is to ask when new data require introduction of an additional parameter, describing a newly discovered physical effect. We review model selection statistics, then focus on the Bayesian evidence, which implements Bayesian analysis at the level of models rather than parameters. We describe our CosmoNest code, the first computationally efficient implementation of Bayesian model selection in a cosmological context. We apply it to recent WMAP satellite data, examining the need for a perturbation spectral index differing from the scaleinvariant (Harrison–Zel'dovich) case.

Journal Article.  4180 words.  Illustrated.

Subjects: Astronomy and Astrophysics ; Geophysics

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