Speaker: Christopher Fonnesbeck

Nowadays, there are many ways of building data science models using Python, including statistical and machine learning methods. I will introduce probabilistic models, which use Bayesian statistical methods to quantify all aspects of uncertainty relevant to your problem, and provide inferences in simple, interpretable terms using probabilities. A particularly flexible form of probabilistic models uses Bayesian nonparametric methods, which allow models to vary in complexity depending on how much data are available. In doing so, they avoid the overfitting that is common in machine learning and statistical modeling. I will demonstrate the basics of Bayesian nonparametric modeling in Python, using the PyMC3 package. Specifically, I will introduce two common types, Gaussian processes and Dirichlet processes, and show how they can be applied easily to realworld problems using two examples.

Slides can be found at: https://speakerdeck.com/pycon2018 and https://github.com/PyCon/2018slides