A "quick" introduction to PyMC3 and Bayesian models

We've all been there, maybe 15 minutes before a meeting, at 4 AM after a party, or simply when we feel too lazy to walk. And even though apps like Uber have made it relatively painless, there are still times when it is necessary or practical to just wait for a taxi. So we wait, impatiently, probably while wondering how much we will have to wait. As the name implies, a generative model is a probability model which is able to generate data that looks a lot like the data we might gather from the phenomenon we're trying to model. In our case, we need a model that generates data that looks like waiting times.