Hamiltonian Monte Carlo (HMC) is a Markov chain Monte Carlo method that allows to sample high dimensional probability measures. It relies on the integration of the Hamiltonian dynamics to propose a move which is then accepted or rejected thanks to a Metropolis procedure. Unbiased sampling is guaranteed by the preservation by the numerical integrators of two key properties of the Hamiltonian dynamics: volume-preservation and reversibility up to momentum reversal. For separable Hamiltonian functions, some standard explicit numerical schemes, such as the St\"ormer--Verlet integrator, satisfy these properties. However, for numerical or physical reasons, one may consider a Hamiltonian function which is nonseparable, in which case the standard numerical schemes which preserve the volume and satisfy reversibility up to momentum reversal are implicit. Actually, when implemented in practice, such implicit schemes may admit many solutions or none, especially when the timestep is too large. We show here how to enforce the numerical reversibility, and thus unbiasedness, of HMC schemes in this context. Numerical results illustrate the relevance of this correction on simple problems.
翻译:哈密顿蒙特卡罗(HMC)是一种马尔可夫链蒙特卡罗方法,可用于采样高维概率测度。它依赖于对哈密顿动力学的积分,以提出移动,然后通过Metropolis程序接受或拒绝。通过数值积分器保持哈密顿动力学的两个关键属性:体积保持和动量反转的可逆性,可以确保无偏见的采样。对于可分离的哈密顿函数,一些标准的显式数值方案,如St\"ormer-Verlet积分器,满足这些属性。然而,由于数值或物理原因,人们可能考虑哈密顿函数是不可分离的情况,这种情况下保持体积并满足动量反转的标准数值方案是隐式的。实际上,在实践中实现这样的隐式方案可能会导致许多或无解,尤其是当时间步骤过大时。我们在此展示如何在这种情况下实现数值上的可逆性,从而确保无偏的HMC方案。数值结果说明了这种修正在简单问题上的相关性。