论文标题
顺序蒙特卡洛家谱与应用的简单条件
Simple conditions for convergence of sequential Monte Carlo genealogies with applications
论文作者
论文摘要
我们介绍了简单的条件，在这些条件下，随着粒子数量的增加，与一类与非中性选择机制相互作用的粒子系统相关的局限性过程是一个时间敏感的金曼合并。顺序的蒙特卡洛算法是流行的方法，用于在诸如采用这种类型的粒子系统的非线性过滤和平滑等问题中近似积分。它们的性能在很大程度上取决于诱发的家谱过程的特性。我们验证了具有广泛的低变化重新采样方案以及带有多项式重新采样的条件顺序蒙特卡洛的标准顺序蒙特卡洛算法的主要结果条件。
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.
