Application of Bayesian model averaging to measurements of the primordial power spectrum
- Astronomy Centre, University of Sussex, Brighton BN1 9QH (United Kingdom)
Cosmological parameter uncertainties are often stated assuming a particular model, neglecting the model uncertainty, even when Bayesian model selection is unable to identify a conclusive best model. Bayesian model averaging is a method for assessing parameter uncertainties in situations where there is also uncertainty in the underlying model. We apply model averaging to the estimation of the parameters associated with the primordial power spectra of curvature and tensor perturbations. We use CosmoNest and MultiNest to compute the model evidences and posteriors, using cosmic microwave data from WMAP, ACBAR, BOOMERanG, and CBI, plus large-scale structure data from the SDSS DR7. We find that the model-averaged 95% credible interval for the spectral index using all of the data is 0.940<n{sub s}<1.000, where n{sub s} is specified at a pivot scale 0.015 Mpc{sup -1}. For the tensors model averaging can tighten the credible upper limit, depending on prior assumptions.
- OSTI ID:
- 21503616
- Journal Information:
- Physical Review. D, Particles Fields, Vol. 82, Issue 10; Other Information: DOI: 10.1103/PhysRevD.82.103533; (c) 2010 American Institute of Physics; ISSN 0556-2821
- Country of Publication:
- United States
- Language:
- English
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