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. We provide a basic introduction to the ideas behind the method and make them tangible by implementing them ourselves using Numpyro. We then present two concrete examples in practice using both Numpyro and PyMC. Namely time-varying coefficient regression and time series forecasting.
Dr. Juan Orduz
Juan is a Mathematician (Ph.D. Humboldt Universität zu Berlin) and data scientist. He is interested in interdisciplinary applications of mathematical methods. In particular, time series analysis, bayesian methods, and causal inference.