  Realizing the full potential of interconnecting the large amounts of data
created in physics experiments, phenomenological models and theory simulations
requires robust tools for statistical inference. Here I review a particularly
promising branch, Bayesian statistics, which over the past decade has found
manifold use in high-energy physics. After a brief introduction to Bayesian
statistics I will present two concrete examples, where Bayesian thinking has
led to progress in understanding strongly interacting matter: unfolding
problems in the form of lattice QCD spectral functions (in spirit similar to
detector corrections), as well as the efficient estimation of
quark-gluon-plasma parameters from a systematic comparison of experimental
heavy-ion collision data and phenomenological models.
