  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.
