PyMC3 (http://pymc-devs.github.io/pymc3/) has all the powerful samplers that Stan has (i.e. NUTS) as well as support for discrete priors. It allows you to specify your models in pure Python, supports matrix algebra, and also has variational inference which allows for large-ish scale machine learning (e.g. here is a blog post where I train a Bayesian Neural Net on MNIST http://twiecki.github.io/blog/2016/07/05/bayesian-deep-learn...). Under the hood, it uses theano for JIT compilation to C or GPU.
I'm one of the core devs.