Bayesian inference in hadronic physics: examples from heavy ion physics

Colliding large nuclei at velocities close to the speed of light produces a plasma of strongly interacting nuclear matter known as quark-gluon plasma. This nuclear plasma can be characterized by macroscopic properties such as its equation of state and viscosity. Bayesian inference provides an important tool to constrain systematically these properties of nuclear matter with measurements from the Relativistic Heavy Ion Collider and the Large Hadron Collider. I will survey different use of Bayesian inference in heavy-ion collisions, including applications of transfer learning, model comparison, closure tests, model averaging and stochastic emulator uncertainty optimization.