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