  In the context of data modeling and comparisons between different fit models,
Bayesian analysis calls that model best which has the largest evidence, the
prior-weighted integral over model parameters of the likelihood function.
Evidence calculations automatically take into account both the usual
chi-squared measure and an Occam factor which quantifies the price for adding
extra parameters. Applying Bayesian analysis to projections onto azimuth of 2D
angular correlations from 200 GeV AuAu collisions, we consider typical model
choices including Fourier series and a Gaussian plus combinations of individual
cosine components. We find that models including a Gaussian component are
consistently preferred over pure Fourier-series parametrizations, sometimes
strongly so. For 0-5% central collisions the Gaussian-plus-dipole model
performs better than Fourier Series models or any other combination of
Gaussian-plus-multipoles.
