Through an adversarial game, the recently proposed generative adversarial networks (GANs) can implicitly learn rich distributions over images, audio, and data which are hard to model with an explicit likelihood. In this talk, I will present a practical Bayesian formulation for unsupervised and semi-supervised learning with GANs. Within this framework, we use stochastic gradient Hamiltonian Monte Carlo for marginalizing parameters. The resulting approach can automatically discover complementary and interpretable generative hypotheses for collections of images. Moreover, by exploring an expressive posterior over these hypotheses, we show that it is possible to achieve state-of-the-art quantitative results on major image classification benchmarks even with less than 1% of the labelled training data.