标题： 概率编程推理的新方法 显示英文标题

标题： A New Approach to Probabilistic Programming Inference

摘要： 我们介绍并展示了一种基于粒子马尔可夫链蒙特卡罗的新方法，用于表达性概率编程语言中的推理。我们的方法简单易实现且易于并行化。它适用于图灵完备的概率编程语言，并支持在使用复杂控制流（包括随机递归）的模型中进行准确推理。它还包括来自贝叶斯非参数统计的原语。实验表明，这种方法可以比之前引入的单站点梅特罗波利斯-黑斯廷斯方法更高效。 显示英文摘要

摘要： We introduce and demonstrate a new approach to inference in expressive probabilistic programming languages based on particle Markov chain Monte Carlo. Our approach is simple to implement and easy to parallelize. It applies to Turing-complete probabilistic programming languages and supports accurate inference in models that make use of complex control flow, including stochastic recursion. It also includes primitives from Bayesian nonparametric statistics. Our experiments show that this approach can be more efficient than previously introduced single-site Metropolis-Hastings methods.