Bayesian model comparison in cosmology with Population Monte Carlo
  We use Bayesian model selection techniques to test extensions of the standard
flat LambdaCDM paradigm. Dark-energy and curvature scenarios, and primordial
perturbation models are considered. To that end, we calculate the Bayesian
evidence in favour of each model using Population Monte Carlo (PMC), a new
adaptive sampling technique which was recently applied in a cosmological
context. The Bayesian evidence is immediately available from the PMC sample
used for parameter estimation without further computational effort, and it
comes with an associated error evaluation. Besides, it provides an unbiased
estimator of the evidence after any fixed number of iterations and it is
naturally parallelizable, in contrast with MCMC and nested sampling methods. By
comparison with analytical predictions for simulated data, we show that our
results obtained with PMC are reliable and robust. The variability in the
evidence evaluation and the stability for various cases are estimated both from
simulations and from data. For the cases we consider, the log-evidence is
calculated with a precision of better than 0.08.
  Using a combined set of recent CMB, SNIa and BAO data, we find inconclusive
evidence between flat LambdaCDM and simple dark-energy models. A curved
Universe is moderately to strongly disfavoured with respect to a flat
cosmology. Using physically well-motivated priors within the slow-roll
approximation of inflation, we find a weak preference for a running spectral
index. A Harrison-Zel'dovich spectrum is weakly disfavoured. With the current
data, tensor modes are not detected; the large prior volume on the
tensor-to-scalar ratio r results in moderate evidence in favour of r=0.
[Abridged]
