2016/07/31 by Esteban Guevara Hidalgo, Takahiro Nemoto, Vivien Lecomte · 22 citations
Mathematics · Physics and Astronomy · #Advanced Thermodynamics and Statistical Mechanics #Applied mathematics #Computer science #Estimator #Finite set #Function (biology) #Large deviations theory #Limit (mathematics) #Mathematical analysis #Mathematical optimization #Mathematics #Physics #Population #Population size #Sample size determination #Selection (genetic algorithm) #Standard deviation #Statistical physics #Statistics #Stochastic processes and statistical mechanics #Theoretical and Computational Physics #cond-mat.stat-mech
paper · pdf · doi:10.1103/physreve.95.062134
published in Physical review. E 95(6), 062134 (American Physical Society) · 12 pages, 10 figures. Second part of pair of companion papers, following Part I arXiv:1607.04752
arxiv created 2017/05/10 · openalex publication_date 2017/06/28 · arxiv updated 2017/07/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
Rare trajectories of stochastic systems are important to understand because of their potential impact. However, their properties are by definition difficult to sample directly. Population dynamics provides a numerical tool allowing their study, by means of simulating a large number of copies of the system, which are subjected to selection rules that favor the rare trajectories of interest. Such algorithms are plagued by finite simulation time and finite population size, effects that can render their use delicate. In this paper, we present a numerical approach which uses the finite-time and finite-size scalings of estimators of the large deviation functions associated to the distribution of rare trajectories. The method we propose allows one to extract the infinite-time and infinite-size limit of these estimators, which-as shown on the contact process-provides a significant improvement of the large deviation function estimators compared to the standard one.