PhysRevD.82.103533.pdf (274.27 kB)
Application of Bayesian model averaging to measurements of the primordial power spectrum
journal contribution
posted on 2023-06-08, 08:29 authored by David Parkinson, Andrew R LiddleCosmological 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
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Physical Review DISSN
15507998External DOI
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10Volume
82Department affiliated with
- Physics and Astronomy Publications
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Article number 103533Full text available
- Yes
Peer reviewed?
- Yes
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2012-02-06First Open Access (FOA) Date
2016-03-22First Compliant Deposit (FCD) Date
2016-11-10Usage metrics
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