Variance Reduction with Array-RQMC for Tau-Leaping Simulation of Stochastic Biological and Chemical Reaction Networks
暂无分享,去创建一个
Pierre L'Ecuyer | Florian Puchhammer | Amal Ben Abdellah | Amal Ben Abdellah | P. L'Ecuyer | F. Puchhammer
[1] P. Marion,et al. A Tool for Custom Construction of QMC and RQMC Point Sets , 2020, MCQMC.
[2] Pierre L'Ecuyer,et al. Array-RQMC for Option Pricing Under Stochastic Volatility Models , 2019, 2019 Winter Simulation Conference (WSC).
[3] R. Baker,et al. Quasi-Monte Carlo Methods Applied to Tau-Leaping in Stochastic Biological Systems , 2018, Bulletin of mathematical biology.
[4] P. L’Ecuyer,et al. Randomized quasi-Monte Carlo: An introduction for practitioners , 2016 .
[5] Bruno Tuffin,et al. Sorting methods and convergence rates for Array-RQMC: Some empirical comparisons , 2016, Math. Comput. Simul..
[6] Pierre L'Ecuyer,et al. Algorithm 958 , 2016, ACM Trans. Math. Softw..
[7] Silvana Ilie,et al. An adaptive tau-leaping method for stochastic simulations of reaction-diffusion systems , 2016 .
[8] Michael B. Giles,et al. Algorithm 955 , 2016, ACM Trans. Math. Softw..
[9] Silvana Ilie,et al. Hybrid stochastic simulation of reaction-diffusion systems with slow and fast dynamics. , 2015, The Journal of chemical physics.
[10] Jae Kyoung Kim,et al. The relationship between stochastic and deterministic quasi-steady state approximations , 2015, BMC Systems Biology.
[11] P. L’Ecuyer,et al. Algorithm 958: Lattice Builder: A General Software Tool for Constructing Rank-1 Lattice Rules , 2015, ACM Trans. Math. Softw..
[12] N. Chopin,et al. Sequential Quasi-Monte Carlo , 2014, 1402.4039.
[13] Wonryull Koh,et al. Improved spatial direct method with gradient-based diffusion to retain full diffusive fluctuations. , 2012, The Journal of chemical physics.
[14] Pierre L'Ecuyer,et al. Variance bounds and existence results for randomly shifted lattice rules , 2012, J. Comput. Appl. Math..
[15] Philipp Thomas,et al. The slow-scale linear noise approximation: an accurate, reduced stochastic description of biochemical networks under timescale separation conditions , 2012, BMC Systems Biology.
[16] Desmond J. Higham,et al. Multilevel Monte Carlo for Continuous Time Markov Chains, with Applications in Biochemical Kinetics , 2011, Multiscale Model. Simul..
[17] Pierre L'Ecuyer,et al. American option pricing with randomized quasi-Monte Carlo simulations , 2010, Proceedings of the 2010 Winter Simulation Conference.
[18] F. Pillichshammer,et al. Digital Nets and Sequences: Discrepancy Theory and Quasi-Monte Carlo Integration , 2010 .
[19] C. Lemieux. Monte Carlo and Quasi-Monte Carlo Sampling , 2009 .
[20] Pierre L'Ecuyer,et al. Quasi-Monte Carlo methods with applications in finance , 2008, Finance Stochastics.
[21] P. L’Ecuyer,et al. On Array-RQMC for Markov Chains: Mapping Alternatives and Convergence Rates , 2008 .
[22] Frances Y. Kuo,et al. Constructing Sobol Sequences with Better Two-Dimensional Projections , 2008, SIAM J. Sci. Comput..
[23] Desmond J. Higham,et al. Modeling and Simulating Chemical Reactions , 2008, SIAM Rev..
[24] Andreas Hellander,et al. Efficient computation of transient solutions of the chemical master equation based on uniformization and quasi-Monte Carlo. , 2008, The Journal of chemical physics.
[25] David F. Anderson. Incorporating postleap checks in tau-leaping. , 2007, The Journal of chemical physics.
[26] Bruno Tuffin,et al. Rare events, splitting, and quasi-Monte Carlo , 2007, TOMC.
[27] Pierre L'Ecuyer,et al. A Randomized Quasi-Monte Carlo Simulation Method for Markov Chains , 2006, Oper. Res..
[28] Pierre L'Ecuyer,et al. Quasi-Monte Carlo Simulation of Discrete-Time Markov Chains on Multidimensional State Spaces , 2006 .
[29] Henryk Wozniakowski,et al. Good Lattice Rules in Weighted Korobov Spaces with General Weights , 2006, Numerische Mathematik.
[30] Pierre L'Ecuyer,et al. Simulation in Java with SSJ , 2005, Proceedings of the Winter Simulation Conference, 2005..
[31] Art B. Owen,et al. Variance with alternative scramblings of digital nets , 2003, TOMC.
[32] C. Rao,et al. Stochastic chemical kinetics and the quasi-steady-state assumption: Application to the Gillespie algorithm , 2003 .
[33] Fred J. Hickernell,et al. Monte Carlo and Quasi-Monte Carlo Methods 2000 , 2002 .
[34] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[35] D. Gillespie. Approximate accelerated stochastic simulation of chemically reacting systems , 2001 .
[36] D. Gillespie. The chemical Langevin equation , 2000 .
[37] Fred J. Hickernell,et al. Extensible Lattice Sequences for Quasi-Monte Carlo Quadrature , 2000, SIAM J. Sci. Comput..
[38] P. L’Ecuyer,et al. Variance Reduction via Lattice Rules , 1999 .
[39] Jirí Matousek,et al. On the L2-Discrepancy for Anchored Boxes , 1998, J. Complex..
[40] P. Hellekalek,et al. Random and Quasi-Random Point Sets , 1998 .
[41] C. Lécot,et al. A Quasi-Monte Carlo Scheme Using Nets for a Linear Boltzmann Equation , 1998 .
[42] A. Owen. Monte Carlo Variance of Scrambled Net Quadrature , 1997 .
[43] A. Owen. Scrambled net variance for integrals of smooth functions , 1997 .
[44] I. Sloan. Lattice Methods for Multiple Integration , 1994 .
[45] W. J. Anderson. Continuous-Time Markov Chains: An Applications-Oriented Approach , 1991 .
[46] P. Glynn,et al. Discrete-time conversion for simulating finite-horizon Markov processes , 1990 .
[47] D. Gillespie. Exact Stochastic Simulation of Coupled Chemical Reactions , 1977 .
[48] A. Abas. The calculation of the solution of multidimensional integral equations with methods Monte Carlo and quasi-Monte Carlo , 2021, T-Comm.
[49] P. L’Ecuyer,et al. On Figures of Merit for Randomly-Shifted Lattice Rules , 2012 .
[50] David F. Anderson,et al. Continuous Time Markov Chain Models for Chemical Reaction Networks , 2011 .
[51] Alexander Keller,et al. Efficient Simultaneous Simulation of Markov Chains , 2008 .
[52] Pierre L’Ecuyer,et al. Random Number Generation , 2008, Encyclopedia of Algorithms.
[53] Pierre L'Ecuyer,et al. Randomized Quasi-Monte Carlo Simulation of Markov Chains with an Ordered State Space , 2006 .
[54] P. L’Ecuyer,et al. A COMBINATION OF RANDOMIZED QUASI-MONTE CARLO WITH SPLITTING FOR RARE-EVENT SIMULATION , 2005 .
[55] Linda R Petzold,et al. The slow-scale stochastic simulation algorithm. , 2005, The Journal of chemical physics.
[56] Pierre L'Ecuyer,et al. Recent Advances in Randomized Quasi-Monte Carlo Methods , 2002 .
[57] F. J. Hickernell. Obtaining O( N - 2+∈ ) Convergence for Lattice Quadrature Rules , 2002 .
[58] Ferenc Szidarovszky,et al. Modeling uncertainty : an examination of stochastic theory, methods, and applications , 2002 .
[59] D. Pollock. Smoothing with Cubic Splines , 1999 .
[60] Pierre L'Ecuyer,et al. Good Parameters and Implementations for Combined Multiple Recursive Random Number Generators , 1999, Oper. Res..
[61] Art B. Owen,et al. Latin supercube sampling for very high-dimensional simulations , 1998, TOMC.
[62] F. J. Hickernell. Lattice rules: how well do they measure up? in random and quasi-random point sets , 1998 .
[63] J. M. Sek,et al. On the L2-discrepancy for anchored boxes , 1998 .
[64] P. Kloeden,et al. Numerical Solutions of Stochastic Differential Equations , 1995 .
[65] Harald Niederreiter,et al. Random number generation and Quasi-Monte Carlo methods , 1992, CBMS-NSF regional conference series in applied mathematics.
[66] W. J. Anderson. Continuous-Time Markov Chains , 1991 .
[67] I. Sobol. On the distribution of points in a cube and the approximate evaluation of integrals , 1967 .