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Creators/Authors contains: "Marsden, Milo"

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  1. ; ; ; (, The Journal of Chemical Physics)
    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. 
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    Free, publicly-accessible full text available August 28, 2026
  2. ; (, The Annals of Applied Probability)
    Free, publicly-accessible full text available February 1, 2026