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Cosmological model selection
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Model selection aims to determine which theoretical models are most plausible given some data, without necessarily asking about the preferred values of the 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 several model selection statistics, and then focus on use of the Bayesian evidence, which implements the usual Bayesian analysis framework at the level of models rather than parameters. We describe our CosmoNest code, which is 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 scale-invariant (Harrison-Zel'dovich) case.
Forward citations
Cited by 4 Pith papers
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General parametrization for energy density of quintessence field
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Flexible dark energy equation of state parametrizations mildly favor dynamical phantom-like behavior at z~1-2, but the preference is only ~2 sigma and sensitive to the model form and extrapolation.
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Observational constraints on early time non-phantom behaviour of dynamical dark energy
Early scaling dark energy is constrained to be less than about one percent at matter-radiation equality and is disfavored by model selection, while late-time CPL dynamics show only a weak preference away from ΛCDM.
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Beyond CPL: Evidence for dynamical dark energy in three-parameter models
Two three-parameter extensions of the mAH dark energy parametrization are compared to LambdaCDM, wCDM, CPL and others using CMB, DESI BAO, H(z), RSD and three SNIa samples, yielding Delta chi-squared improvements of 6...
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