In machine learning and statistics, probabilistic inference involving
multimodal distributions is quite difficult. This is especially true in high
dimensional problems, where most existing algorithms cannot easily move from
one mode to another. To address this issue, we propose a novel
研究了一种基于 Riemann 流形的 Hamiltonian Monte Carlo 采样算法，通过自适应方法规避了调整提议密度的需求，使得即使在高维状态空间建模中，也能更加高效地采样，该方法在众多实证分析中表现出较大的优越性，Matlab 代码在 http://www.dcs.gla.ac.uk/inference/rmhmc 可复现。