Probabilistic programming allows for flexible specification of Bayesian statistical models in code. PyMC3 is a new, open-source probabilistic programmer framework with an intuitive, readable and concise, yet powerful, syntax that is close to the natural notation statisticians use to describe models. It features next-generation fitting techniques, such as the No U-Turn Sampler, that allow fitting complex models with thousands of parameters without specialized knowledge of fitting algorithms.
PyMC3 has recently seen rapid development. With the addition of two new major features: automatic transforms and missing value imputation, PyMC3 has become ready for wider use. PyMC3 is now refined enough that adding features is easy, so we don’t expect adding features in the future will require drastic changes. It has also become user friendly enough for a broader audience. Automatic transformations mean NUTS and find_MAP work with less effort, and friendly error messages mean its easy to diagnose problems with your model.
Thus, Thomas, Chris and I are pleased to announce that PyMC3 is now in Beta.
Try it out!
To get started with PyMC3, I recommend the Tutorial.
If you have a question, we are quite responsive on Stack Overflow and Twitter (@johnsalvatier, @fonnesbeck and @twiecki). If you have a bug report please post it to our issues list.