Dr. Juan Orduz

Causal inference asks the hardest question in data science: "What would have happened if things were different?" While traditional methods often rely on rigid rules, statistical tests or "black box" adjustments, Probabilistic Programming Languages (PPLs) like PyMC and NumPyro offer a transparent,...

This talk presents variational inference as a tool to scale probabilistic models. We describe practical examples with NumPyro and PyMC to demonstrate this method, going through the main concepts and diagnostics.

We provide an introduction to implementing time series forecasting modeling in NumPyro. This allows us to write custom models for which we can have complete control.

In this talk, we explore a new method to approximate Gaussian processes using spectral analysis methods, known as the Hilbert Space Gaussian process (HSGP) approximation. This technique allows us to use and fit Gaussian processes at scale for concrete applications.