A popular method for likelihood-free inference is approximate Bayesian computation sequential Monte Carlo (ABC-SMC) algorithms. These approximate the posterior using a population of particles, which are updated using Markov kernels. Several such kernels have been proposed. In this paper we review these, highlighting some less well known choices, and proposing some novel options. Further, we conduct an extensive empirical comparison of kernel choices. Our results suggest using a one-hit kernel with a mixture proposal as a default choice.

翻译：近似贝叶斯计算序贯蒙特卡洛（ABC-SMC）算法是一种常用的无似然推断方法。该方法通过粒子群逼近后验分布，并利用马尔可夫核进行粒子更新。目前已提出多种此类核函数。本文系统综述了现有核函数，重点介绍了一些较少被关注的选项，并提出若干新颖的核函数设计方案。此外，我们开展了核函数选择的广泛实证比较。实验结果表明，采用混合提议分布的单次命中核函数可作为默认选择。