Application of Bayesian model averaging to measurements of the
  primordial power spectrum
  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.
