Probabilistic programming products

Algorithmic innovations like NUTS and ADVI, and their inclusion in end user probabilistic programming systems such as PyMC3 and Stan, have made Bayesian inference a more robust, practical and computationally affordable approach. I will review inference and the algorithmic options, before describing two prototypes that depend on these innovations: one that supports decisions about consumer loans and one that models the future of the NYC real estate market. These prototypes highlight the advantages and use cases of the Bayesian approach, which include domains where data is scarce, where prior institutional knowledge is important, and where quantifying risk is crucial. Finally I'll touch on some of the engineering and UX challenges of using PyMC3 and Stan models not only for offline tasks like natural science and business intelligence, but in live end-user products.