Chris Fonnesbeck
Chris is a Principal Quantitative Analyst at PyMC Labs and an Adjoint Associate Professor at the Vanderbilt University Medical Center, with 20 years of experience as a data scientist in academia, industry, and government. He is interested in computational statistics, machine learning, Bayesian methods, and applied decision analysis. He hails from Vancouver, Canada and received his Ph.D. from the University of Georgia.
Sessions
When Bayesian modeling scales up to large datasets, traditional MCMC methods can become impractical due to their computational demands. Variational Inference (VI) offers a scalable alternative, trading exactness for speed while retaining the essence of Bayesian inference.
In this tutorial, we’ll explore how to implement and compare VI techniques in PyMC, including the Adaptive Divergence Variational Inference (ADVI) and the cutting-edge Pathfinder algorithm.
Starting with simple models like linear regression, we’ll gradually introduce more complex, real-world applications, comparing the performance of VI against Markov Chain Monte Carlo (MCMC) to understand the trade-offs in speed and accuracy.
This tutorial will arm participants with practical tools to deploy VI in their workflows and help answer pressing questions, like "What do I do when MCMC is too slow?", or "How does VI compare to MCMC in terms of approximation quality?".