Recent advances in stochastic gradient techniques have made it possible to
estimate posterior distributions from large datasets via Markov Chain Monte
Carlo (MCMC). However, when the target posterior is multimodal, mixing
performance is often poor. This results in inadequate exploration of the
posterior distribution. A framework is proposed to improve the sampling
efficiency of stochastic gradient MCMC, based on Hamiltonian Monte Carlo. A
generalized kinetic function is leveraged, delivering superior stationary
mixing, especially for multimodal distributions. Techniques are also discussed
to overcome the practical issues introduced by this generalization. It is shown
that the proposed approach is better at exploring complex multimodal posterior
distributions, as demonstrated on multiple applications and in comparison with
other stochastic gradient MCMC methods.

本文提出了利用 Hamiltonian Monte Carlo 方法的广义运动函数来改进随机梯度马尔可夫蒙特卡罗采样的效率，并讨论了克服这种泛化所引入的实际问题的技术。实验证明，该方法在探索复杂的多峰后验分布方面表现优秀。