Bayes Reading Group: Milo Marsden (Stanford)

Friday Sep 27, 2024, 11:00 AM → 12:00 PM America/New_York

3rd Floor Conference Room (162 Fifth Avenue)

Discussion Lead: Milo Marsden (Stanford)

Topic: GIST sampler

Title: Incorporating Local Step-Size Adaptivity into the No-U-Turn Sampler using Gibbs Self Tuning

Abstract: Adapting the step size locally in the no-U-turn sampler (NUTS) is challenging because the step-size and path-length tuning parameters are interdependent. The determination of an optimal path length requires a predefined step size, while the ideal step size must account for errors along the selected path. Ensuring reversibility further complicates this tuning problem. In this paper, we present a method for locally adapting the step size in NUTS that is an instance of the Gibbs self-tuning (GIST) framework. Our approach guarantees reversibility with an acceptance probability that depends exclusively on the conditional distribution of the step size. We validate our step-size-adaptive NUTS method on Neal’s funnel density and a high-dimensional normal distribution, demonstrating its effectiveness in challenging scenarios.