Interacting particle populations undergoing repeated mutation and fitness-based selection steps model genetic evolution, and describe a broad class of sequential Monte Carlo methods. The genealogical tree embedded into the system is important in both applications. Under neutrality, when fitnesses of particles and their parents are independent, rescaled genealogies are known to converge to Kingman's coalescent. Recent work established convergence under non-neutrality, but only for finite-dimensional distributions. We prove weak converge of non-neutral genealogies on the space of cadlag paths under standard assumptions, enabling analysis of the whole genealogical tree. The proof relies on a conditional coupling in a random environment.
翻译:反复突变和以健身为基础的选择步骤模型遗传演化的间粒粒子群,并描述一系列广泛的相继蒙特卡洛方法。在两种应用中,嵌入该系统的基因树都很重要。在中性情况下,当粒子及其父母的适合性是独立的,则已知重新定序的基因动物会与Kingman的日亮相交汇。最近的工作在非中性下建立了趋同,但仅限于有限尺寸的分布。我们证明,根据标准假设,在焦渣路径空间上的非中性基因树群的趋同很弱,有利于对整个基因树进行分析。证据依赖于随机环境中的有条件结合。