Parkinson, David and Liddle, Andrew R (2010) Application of Bayesian model averaging to measurements of the primordial power spectrum. Physical Review D, 82 (10). ISSN 15507998

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Abstract

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<ns<1.000, where ns is specified at a pivot scale 0.015Mpc-1. For the tensors model averaging can tighten the credible upper limit, depending on prior assumptions.

Item Type:	Article
Additional Information:	Article number 103533
Schools and Departments:	School of Mathematical and Physical Sciences > Physics and Astronomy
Depositing User:	Andrew Liddle
Date Deposited:	06 Feb 2012 21:00
Last Modified:	03 Jul 2019 01:51
URI:	http://sro.sussex.ac.uk/id/eprint/29085

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