论文标题
邀请顺序蒙特卡洛采样器
An invitation to sequential Monte Carlo samplers
论文作者
论文摘要
统计学家经常使用蒙特卡洛方法来近似概率分布，主要与马尔可夫链蒙特卡洛和重要性抽样相比。顺序的蒙特卡洛采样器是一类算法，将这两种技术结合到近似于兴趣分布及其正常化常数。这些采样器源于状态空间模型的粒子过滤，并已成为一般且可扩展的采样技术。本文介绍了连续的蒙特卡洛采样器及其可能的实现，认为尽管它们能够执行顺序推断并利用其他潜在的收益以及利用并行处理资源，但它们仍在统计数据中不足。
Statisticians often use Monte Carlo methods to approximate probability distributions, primarily with Markov chain Monte Carlo and importance sampling. Sequential Monte Carlo samplers are a class of algorithms that combine both techniques to approximate distributions of interest and their normalizing constants. These samplers originate from particle filtering for state space models and have become general and scalable sampling techniques. This article describes sequential Monte Carlo samplers and their possible implementations, arguing that they remain under-used in statistics, despite their ability to perform sequential inference and to leverage parallel processing resources among other potential benefits.
