Probabilistic programming is a method for Bayesian statistics in which statistical models are encoded as programs. In this talk I will introduce probabilistic programming and the problem of inference. I will then present two kinds of semantics for probabilistic programs: one based on measures and probability kernels, and one based on event structures and data-flow graphs. I will explain the advantages of each, and discuss some further challenges: data flow, higher-order functions, stateful programming for Bayesian nonparametric models.
