We present simple conditions under which the limiting genealogical process associated with a class of interacting particle systems with non-neutral selection mechanisms, as the number of particles grows, is a time-rescaled Kingman coalescent. Sequential Monte Carlo algorithms are popular methods for approximating integrals in problems such as non-linear filtering and smoothing which employ this type of particle system. Their performance depends strongly on the properties of the induced genealogical process. We verify the conditions of our main result for standard sequential Monte Carlo algorithms with a broad class of low-variance resampling schemes, as well as for conditional sequential Monte Carlo with multinomial resampling.

翻译：我们提出的简单条件是,随着微粒的增加,与非中性选择机制有关的一类交互式粒子系统相关的有限基因过程,是一个经过时间调整的国王月亮,顺序的蒙特卡洛算法是使用这种粒子系统的非线性过滤和通畅等问题中近似组成部分的常用方法,其性能在很大程度上取决于诱发的基因过程的特性。我们核查了我们的主要结果的条件,即按低变率重新采样计划等一系列标准顺序的蒙特卡洛算法,以及有条件的连续的蒙特卡洛算法和多义性重采。