GIST: A Framework for Self-Tuning Hamiltonian Monte Carlo by Gibbs Sampling Tuning Parameters

This paper introduces GIST (Gibbs self-tuning), a novel framework for locally adaptive Hamiltonian Monte Carlo (HMC) sampling, which expands the state space to include tuning parameters as auxiliary variables, enabling their adaptive sampling based on position and momentum.