A selective view of stochastic inference and mod-eling problems in nanoscale biophysics

作     者：KOU S.C. 

作者机构：Department of StatisticsHarvard UniversityCambridgeMA 02138USA 

出 版 物：《Science China Mathematics》 (中国科学：数学（英文版）)

年 卷 期：2009年第52卷第6期

页      面：1181-1211页

核心收录：

学科分类：02[经济学] 0202[经济学-应用经济学] 020208[经济学-统计学] 07[理学] 0714[理学-统计学（可授理学、经济学学位）] 070103[理学-概率论与数理统计] 0701[理学-数学] 

基　　金：supported by the United States National Science Fundation Career Award (Grant No. DMS-0449204) 

主　　题：likelihood analysis Bayesian data augmentation semi-and non-parametric inference single-molecule experiment subdiffusion generalized Langevin equation fractional Brownian motion stochastic network enzymatic reaction 

摘      要：Advances in nanotechnology enable scientists for the first time to study biological pro-cesses on a nanoscale molecule-by-molecule *** also raise challenges and opportunities for statisticians and applied *** exemplify the stochastic inference and modeling problems in the field,this paper discusses a few selected cases,ranging from likelihood inference,Bayesian data augmentation,and semi-and non-parametric inference of nanometric biochemical systems to the uti-lization of stochastic integro-differential equations and stochastic networks to model single-molecule biophysical *** discuss the statistical and probabilistic issues as well as the biophysical motivation and physical meaning behind the problems,emphasizing the analysis and modeling of real experimental data.