序贯蒙特卡罗方法的并行实现：岛粒子模型
On parallel implementation of Sequential Monte Carlo methods: the island particle model
摘要：
相互作用粒子系统对Feynman-Kac半群的逼近是一个非常活跃的研究领域，在许多不同的领域都有应用。在本文中，我们研究这种近似的并行化。总颗粒群被分成子群，称为\emph{islands}。每个岛中的粒子遵循通常的选择/突变动力学。我们表明，每个岛屿的演变也是由一个费曼-卡茨半群，其过渡和潜在的可以明确地与原问题。因此，可以在岛水平上使用Feynman-Kac半群的相同遗传类型近似;每个岛可以经历选择/突变算法。我们调查了每个岛屿内的总体规模和岛屿数量的影响，并研究了不同类型的相互作用。我们发现在何种条件下引入岛屿之间的相互作用是有益的。理论结果得到了Monte Carlo实验的支持。
英文摘要：
The approximation of the Feynman-Kac semigroups by systems of interacting particles is a very active research field, with applications in many different areas. In this paper, we study the parallelization of such approximations. The total population of particles is divided into sub-populations, referred to as \emph{islands}. The particles within each island follow the usual selection / mutation dynamics. We show that the evolution of each island is also driven by a Feynman-Kac semigroup, whose transition and potential can be explicitly related to ones of the original problem. Therefore, the same genetic type approximation of the Feynman-Kac semi-group may be used at the island level; each island might undergo selection / mutation algorithm. We investigate the impact of the population size within each island and the number of islands, and study different type of interactions. We find conditions under which introducing interactions between islands is beneficial. The theoretical results are supported by some Monte Carlo experiments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
