Bayesian model selection and isocurvature perturbations
  Present cosmological data are well explained assuming purely adiabatic perturbations, but an admixture of isocurvature perturbations is also permitted. We use a Bayesian framework to compare the performance of cosmological models including isocurvature modes with the purely adiabatic case; this framework automatically and consistently penalizes models which use more parameters to fit the data. We compute the Bayesian evidence for fits to a dataset comprised of WMAP and other microwave anisotropy data, the galaxy power spectrum from 2dFGRS and SDSS, and Type Ia supernovae luminosity distances. We find that Bayesian model selection favours the purely adiabatic models, but so far only at low significance. 
Maria Beltran, Juan Garcia-Bellido, Julien Lesgourgues, Andrew R   Liddle and Anze Slosar