PyData NYC 2024

Gaussian processes (GPs) are a powerful Bayesian approach for quantifying uncertainty and making probabilistic inferences, especially when dealing with complex, non-linear relationships in regression and classification.
This tutorial will introduce you to the flexibility of GPs in handling diverse data problems, including the new Hilbert space Gaussian process (HSGP) approximation, which scales GPs to large datasets.
By the end, you will be equipped to specify, fit, and validate GP models using PyMC, with a special focus on a real-world sports analytics case study.

Live recording of the Learning Bayesian Statistics Podcast

Live recording of the Learning Bayesian Statistics Podcast