Description: Actuaries use data, math, intuition, and domain expertise to make decisions under uncertainty. Bayesian models formalize the techniques many of us use every day. While many Bayesian models are impossible to solve analytically, new tools allow us to create rich Bayesian models and use computers to evaluate them quickly. In this session we will build several Bayesian models using Python and pymc3, a probabilistic programming language, to demonstrate Bayesian techniques that can be applied to actuarial work.