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GIST: Gibbs self-tuning for locally adaptive Hamiltonian Monte Carlo
April 24, 2024, 4:46 a.m. | Nawaf Bou-Rabee, Bob Carpenter, Milo Marsden
stat.ML updates on arXiv.org arxiv.org
Abstract: We present a novel and flexible framework for localized tuning of Hamiltonian Monte Carlo samplers by sampling the algorithm's tuning parameters conditionally based on the position and momentum at each step. For adaptively sampling path lengths, we show that randomized Hamiltonian Monte Carlo, the No-U-Turn Sampler, and the Apogee-to-Apogee Path Sampler all fit within this unified framework as special cases. The framework is illustrated with a simple alternative to the No-U-Turn Sampler for locally adapting …
abstract algorithm arxiv framework gibbs hamiltonian monte carlo math.st novel parameters path sampling show stat.co stat.ml stat.th the algorithm type
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