Discontinuous Hamiltonian Monte Carlo for discrete parameters and discontinuous likelihoods
提出一种扩展的哈密顿蒙特卡洛方法，能高效采样具有不连续密度的目标分布，尤其适用于序数参数，通过将概率质量函数嵌入连续空间实现，并开发了首个精确保持哈密顿量的数值求解器。
Summary Hamiltonian Monte Carlo has emerged as a standard tool for posterior computation. In this article we present an extension that can efficiently explore target distributions with discontinuous densities. Our extension in particular enables efficient sampling from ordinal parameters through the embedding of probability mass functions into continuous spaces. We motivate our approach through a theory of discontinuous Hamiltonian dynamics and develop a corresponding numerical solver. The proposed solver is the first of its kind, with a remarkable ability to exactly preserve the Hamiltonian. We apply our algorithm to challenging posterior inference problems to demonstrate its wide applicability and competitive performance.