Hamiltonian Monte Carlo (HMC) is a powerful algorithm to sample latent variables from Bayesian models. The advent of probabilistic programming languages (PPLs) frees users from writing inference algorithms and lets users focus on modeling. However, many models are difficult for HMC to solve directly, which often require tricks like model reparameterization. We are motivated by the fact that many of those models could be simplified by marginalization. We propose to use automatic marginalization as part of the sampling process using HMC in a graphical model extracted from a PPL, which substantially improves sampling from real-world hierarchical models.

翻译：哈密顿蒙特卡洛（HMC）是一种从贝叶斯模型中抽取潜变量的强大算法。概率编程语言（PPL）的出现使用户无需编写推断算法，可专注于建模。然而，许多模型难以通过HMC直接求解，往往需要模型重参数化等技巧。我们注意到，这些模型中的许多可以通过边际化来简化。为此，我们提出在从概率编程语言提取的图模型中，将自动边际化作为使用HMC进行采样流程的一部分，从而显著提升从真实层次模型中采样的效果。