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Model selection in cosmology

journal contribution
posted on 2023-06-07, 13:49 authored by Andrew Liddle, Pia Mukherjee, David Parkinson
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.

History

Publication status

  • Published

Journal

Astronomy and Geophysics

ISSN

1366-8781

Issue

4

Volume

47

Page range

4.30-4.33

Department affiliated with

  • Physics and Astronomy Publications

Full text available

  • Yes

Peer reviewed?

  • Yes

Legacy Posted Date

2007-02-28

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