Cool! Thanks for sharing. I would also like to add, Stan is not that hard. Conceptually it's a bit of a jump, especially if you're coming from comp sci not sta

Cool! Thanks for sharing. I would also like to add, Stan is not that hard. Conceptually it's a bit of a jump, especially if you're coming from comp sci not statistics, but I think it's really worth it. Here are some helpful links: Stan website: http://mc-stan.org/ http://mc-stan.org/ A primer on Stan: http://www.stat.columbia.edu/~gelman/research/published/stan_jebs_2.pdf http://www.stat.columbia.edu/~gelman/research/published/stan... The Python interface PyStan documentation: https://pystan.readthedocs.io/en/latest/ https://pystan.readthedocs.io/en/latest/ A case using Stan in a real startup's product: https://www.smartly.io/blog/tutorial-how-we-productized-bayesian-revenue-estimation-with-stan https://www.smartly.io/blog/tutorial-how-we-productized-baye...