>>> what is the mnist digits equivalent toy problem of probabilistic programming? I wish it were that simple! Unfortunately, the only way to really get an intu

>>> what is the mnist digits equivalent toy problem of probabilistic programming? I wish it were that simple! Unfortunately, the only way to really get an intuitive feel for computational probability is going through the exercises. Before even starting PyMC3, I'd systematically go through some classic problems in probability theory such as the birthday paradox, sleeping beauty problem, 3 prisoner's, etc. And implement them directly in code, without using any dependencies. Here, for example, is Monte Hall in Golang. You can quickly see how trivial it is to extend the game from 3 doors to 99+ doors. And how a derivation might follow: https://play.golang.org/p/RGkJ9dToda9 https://play.golang.org/p/RGkJ9dToda9 Only after you get a feel for going from probability spaces to events to random variables can you begin to understand the power behind taking human error out of inference that probabilistic environments provide. Best of luck ;)