pith. sign in

arxiv: 2404.15253 · v4 · pith:NFYFZYOBnew · submitted 2024-04-23 · 📊 stat.CO · math.ST· stat.ML· stat.TH

GIST: Gibbs self-tuning for locally adaptive Hamiltonian Monte Carlo

classification 📊 stat.CO math.STstat.MLstat.TH
keywords gibbsgisthamiltonianlocallypathadaptivecarloframework
0
0 comments X
read the original abstract

We introduce a novel and flexible framework for constructing locally adaptive Hamiltonian Monte Carlo (HMC) samplers by Gibbs sampling the algorithm's tuning parameters conditionally based on the position and momentum at each step. For adaptively sampling path lengths, our Gibbs self-tuning (GIST) approach encompasses randomized HMC, multinomial HMC, the No-U-Turn Sampler (NUTS), and the Apogee-to-Apogee Path Sampler as special cases. We exemplify the GIST framework with a novel alternative to NUTS for locally adapting path lengths, evaluated with an exact Hamiltonian for a high-dimensional, ill-conditioned Gaussian measure and with the leapfrog integrator for a suite of diverse models.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. A Theoretical Comparison of No-U-Turn Sampler Variants: Necessary and Sufficient Convergence Conditions and Mixing Time Analysis under Gaussian Targets

    stat.ML 2026-03 unverdicted novelty 8.0

    NUTS-mul and NUTS-BPS show nearly identical qualitative ergodicity behavior depending on target tails, with both mixing in O(d^{1/4}) time for Gaussians but smaller constants for NUTS-BPS.

  2. Adaptive tuning of Hamiltonian Monte Carlo methods

    stat.CO 2025-06 conditional novelty 5.0

    ATune combines Gaussian theoretical analysis with burn-in simulation data to select system-specific splitting integrators and hyperparameter credible intervals for improved HMC stability and performance.