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 path lengths.

翻译：我们提出了一种新颖且灵活的框架，通过基于每一步的位置和动量对算法调参参数进行条件采样，实现哈密顿蒙特卡洛采样器的局部调谐。在自适应采样路径长度方面，我们证明了随机化哈密顿蒙特卡洛、无回转采样器和远点间路径采样器均可作为该统一框架的特例。该框架通过一种替代无回转采样器的简单方法实现路径长度的局部自适应，并对其进行了展示。