Probabilistic programming libraries like PyMC3, PyStan, and Edward provide flexible ways for users to define Bayesian models, and powerful algorithms for generating samples from the posterior distribution of these models in the presence of observed data. These posterior samples are naturally high dimensional, and each library has a different strategy for handling and inspecting this data. ArviZ is an open source collaboration between PyMC3 and PyStan developers that uses xarray as a common data structure. ArviZ implements common visualization and criticism tasks, useful in making sense of these models.

This talk will give an introduction to Bayesian modeling, with the sorts of problems you might use it for. We will look at how xarray provides an intuitive way to represent and manipulate tidy labelled data while preserving its natural high-dimensional nature. Along the way, we will take a tour of the Python probabilistic programming ecosystem, showing how ArviZ can produce beautiful plots and analysis using the output of many different libraries.

This talk assumes no special background, and would be great for anyone with an interest in probabilistic programming, handling high dimensional data, visualizing uncertainty, or evaluating Bayesian models.