标题： 随机可微概率程序 显示英文标题

标题： Stochastically Differentiable Probabilistic Programs

摘要： 具有混合支持（包含连续和离散潜在随机变量）的概率程序在许多概率编程系统（PPSs）中很常见。 然而，离散随机变量的存在禁止了许多基于梯度的基本推理引擎，这使得在这些模型上的推理过程特别具有挑战性。 现有的PPSs要么要求用户手动对离散变量进行边缘化，要么需要通过分别对离散和连续变量运行推理来执行组合推理。 前者在大多数情况下不可行，而后者则有一些根本性的缺点。 我们提出了一种新颖的方法，使用随机梯度马尔可夫链蒙特卡洛算法族，在此类程序中高效且稳健地进行推理。 我们在几种重要的具有混合支持的概率程序案例中将我们的基于随机梯度的推理算法与传统基线进行了比较，并证明它优于现有的组合推理基线，并且在较少的编程工作量和较低的计算成本下，效果几乎与对程序的边缘化版本进行推理一样好。 显示英文摘要

摘要： Probabilistic programs with mixed support (both continuous and discrete latent random variables) commonly appear in many probabilistic programming systems (PPSs). However, the existence of the discrete random variables prohibits many basic gradient-based inference engines, which makes the inference procedure on such models particularly challenging. Existing PPSs either require the user to manually marginalize out the discrete variables or to perform a composing inference by running inference separately on discrete and continuous variables. The former is infeasible in most cases whereas the latter has some fundamental shortcomings. We present a novel approach to run inference efficiently and robustly in such programs using stochastic gradient Markov Chain Monte Carlo family of algorithms. We compare our stochastic gradient-based inference algorithm against conventional baselines in several important cases of probabilistic programs with mixed support, and demonstrate that it outperforms existing composing inference baselines and works almost as well as inference in marginalized versions of the programs, but with less programming effort and at a lower computation cost.