Methods for Scalable Probabilistic Inference - IPAM at UCLA

Explore methods for scalable probabilistic inference in astrophysics through this 46-minute conference talk by Dan Foreman-Mackey of the Flatiron Institute. Delivered at IPAM's Workshop III on Source Inference and Parameter Estimation in Gravitational Wave Astronomy, the presentation delves into recent developments in probabilistic programming that enable rigorous Bayesian inference with large datasets and complex models. Learn about scalable methods for time series analysis using Gaussian Processes, and discover open-source tools and computational techniques for accelerating inference in astrophysics. The talk covers topics such as high-dimensional integrals, Monte Carlo methods, gradient-based sampling, Hamiltonian sampling, automatic differentiation, and practical applications in exoplanet research. Gain insights into the challenges and solutions for handling large-scale probabilistic modeling in modern astrophysical data analysis pipelines.