Stochastic averaging and sensitivity analysis for two scale reaction networks.
暂无分享,去创建一个
[1] Bernd A. Berg. Markov Chain Monte Carlo Simulations and Their Statistical Analysis , 2004 .
[2] Dionisios G. Vlachos,et al. Parametric sensitivity analysis for biochemical reaction networks based on pathwise information theory , 2013, BMC Bioinformatics.
[3] Charles J. Geyer,et al. Introduction to Markov Chain Monte Carlo , 2011 .
[4] Ting Wang,et al. Efficiency of the Girsanov Transformation Approach for Parametric Sensitivity Analysis of Stochastic Chemical Kinetics , 2014, SIAM/ASA J. Uncertain. Quantification.
[5] Muruhan Rathinam,et al. Efficient computation of parameter sensitivities of discrete stochastic chemical reaction networks. , 2010, The Journal of chemical physics.
[6] Abhijit Chatterjee,et al. An overview of spatial microscopic and accelerated kinetic Monte Carlo methods , 2007 .
[7] Richard L. Tweedie,et al. Markov Chains and Stochastic Stability , 1993, Communications and Control Engineering Series.
[8] Donald L. Iglehart,et al. Simulation Output Analysis Using Standardized Time Series , 1990, Math. Oper. Res..
[9] Journal of Chemical Physics , 1932, Nature.
[10] Linda R Petzold,et al. Efficient step size selection for the tau-leaping simulation method. , 2006, The Journal of chemical physics.
[11] Can Huang,et al. Strong Convergence and Speed up of Nested Stochastic Simulation Algorithm , 2014 .
[12] David F Anderson,et al. A modified next reaction method for simulating chemical systems with time dependent propensities and delays. , 2007, The Journal of chemical physics.
[13] Qing Zhang,et al. Continuous-Time Markov Chains and Applications: A Two-Time-Scale Approach , 2012 .
[14] Christos Alexopoulos,et al. Implementing the batch means method in simulation experiments , 1996, Winter Simulation Conference.
[15] E Weinan,et al. Nested stochastic simulation algorithms for chemical kinetic systems with multiple time scales , 2007, J. Comput. Phys..
[16] Journal of Computer-Aided Materials Design , 2005 .
[17] D G Vlachos,et al. Overcoming stiffness in stochastic simulation stemming from partial equilibrium: a multiscale Monte Carlo algorithm. , 2005, The Journal of chemical physics.
[18] D. Gillespie,et al. Avoiding negative populations in explicit Poisson tau-leaping. , 2005, The Journal of chemical physics.
[19] Yang Cao,et al. Multiscale stochastic simulation algorithm with stochastic partial equilibrium assumption for chemically reacting systems , 2005 .
[20] P. B. Warren,et al. Steady-state parameter sensitivity in stochastic modeling via trajectory reweighting. , 2012, The Journal of chemical physics.
[21] David F. Anderson,et al. An Efficient Finite Difference Method for Parameter Sensitivities of Continuous Time Markov Chains , 2011, SIAM J. Numer. Anal..
[22] Jacob A. McGill,et al. Efficient gradient estimation using finite differencing and likelihood ratios for kinetic Monte Carlo simulations , 2012, J. Comput. Phys..
[23] T. Kurtz,et al. Separation of time-scales and model reduction for stochastic reaction networks. , 2010, 1011.1672.
[24] Muruhan Rathinam,et al. A pathwise derivative approach to the computation of parameter sensitivities in discrete stochastic chemical systems. , 2012, The Journal of chemical physics.
[25] M. Khammash,et al. The finite state projection algorithm for the solution of the chemical master equation. , 2006, The Journal of chemical physics.
[26] Muruhan Rathinam,et al. Stiffness in stochastic chemically reacting systems: The implicit tau-leaping method , 2003 .
[27] S. Varadhan,et al. Central limit theorem for additive functionals of reversible Markov processes and applications to simple exclusions , 1986 .
[28] Peter W. Glynn,et al. Likelihood ratio gradient estimation for stochastic systems , 1990, CACM.
[29] Markus Hegland,et al. An optimal Finite State Projection Method , 2010, ICCS.
[30] C. D. Meyer,et al. Sensitivity of the stationary distribution vector for an ergodic Markov chain , 1986 .
[31] Peter W. Glynn,et al. Stochastic Simulation: Algorithms and Analysis , 2007 .
[32] Yannis Pantazis,et al. Path-Space Information Bounds for Uncertainty Quantification and Sensitivity Analysis of Stochastic Dynamics , 2016, SIAM/ASA J. Uncertain. Quantification.
[33] D G Vlachos,et al. Steady state likelihood ratio sensitivity analysis for stiff kinetic Monte Carlo simulations. , 2015, The Journal of chemical physics.
[34] Mustafa Khammash,et al. An efficient and unbiased method for sensitivity analysis of stochastic reaction networks , 2014, Journal of The Royal Society Interface.
[35] S. Ethier,et al. Markov Processes: Characterization and Convergence , 2005 .
[36] Yiannis Kaznessis,et al. Accurate hybrid stochastic simulation of a system of coupled chemical or biochemical reactions. , 2005, The Journal of chemical physics.
[37] D. Vlachos,et al. Binomial distribution based tau-leap accelerated stochastic simulation. , 2005, The Journal of chemical physics.
[38] Elizabeth Skubak Wolf,et al. Hybrid pathwise sensitivity methods for discrete stochastic models of chemical reaction systems. , 2014, The Journal of chemical physics.
[39] Adam P. Arkin,et al. Efficient stochastic sensitivity analysis of discrete event systems , 2007, J. Comput. Phys..
[40] Charles J. Geyer,et al. Practical Markov Chain Monte Carlo , 1992 .
[41] Michael A. Gibson,et al. Efficient Exact Stochastic Simulation of Chemical Systems with Many Species and Many Channels , 2000 .
[42] A. Sokal. Monte Carlo Methods in Statistical Mechanics: Foundations and New Algorithms , 1997 .
[43] M. Khammash,et al. Sensitivity analysis for stochastic chemical reaction networks with multiple time-scales , 2013, 1310.1729.
[44] V. Climenhaga. Markov chains and mixing times , 2013 .
[45] David F. Anderson,et al. Continuous Time Markov Chain Models for Chemical Reaction Networks , 2011 .
[46] D. Gillespie. Approximate accelerated stochastic simulation of chemically reacting systems , 2001 .
[47] Linda R Petzold,et al. The slow-scale stochastic simulation algorithm. , 2005, The Journal of chemical physics.
[48] K. Burrage,et al. Binomial leap methods for simulating stochastic chemical kinetics. , 2004, The Journal of chemical physics.