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Simulation with Fluctuating and Singular Rates

Published online by Cambridge University Press:  03 June 2015

Farzin Barekat*
Affiliation:
Mathematics Department, University of California at Los Angeles, Los Angeles, CA 90095-1555, USA
Russel Caflisch*
Affiliation:
Mathematics Department, University of California at Los Angeles, Los Angeles, CA 90095-1555, USA
*
Corresponding author.Email:caflisch@math.ucla.edu
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Abstract

In this paper we present a method to generate independent samples for a general random variable, either continuous or discrete. The algorithm is an extension of the Acceptance-Rejection method, and it is particularly useful for kinetic simulation in which the rates are fluctuating in time and have singular limits, as occurs for example in simulation of recombination interactions in a plasma. Although it depends on some additional requirements, the new method is easy to implement and rejects less samples than the Acceptance-Rejection method.

Type
Research Article
Copyright
Copyright © Global Science Press Limited 2014

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References

[1]Bird, G.A., Molecular Gas Dynamics and the Direct Simulation of Gas Flows, Claredon, Oxford (1994).Google Scholar
[2]Bortz, A.B., Kalos, M.H., and Lebowitz, J.L., A new algorithm for Monte Carlo simulation of Ising spin systems, J. Comp. Phys., 17 (1975), 1018.CrossRefGoogle Scholar
[3]Caflisch, R.E., Monte Carlo and quasi-Monte Carlo methods, Acta Numerica, 7 (1998), 149.Google Scholar
[4]Cao, Y., Li, H., and Petzold, L.R., Efficient formulation of the stochastic simulation algorithm for chemically reacting systems, J. Chem. Phys., 121 (2004), 40594067.Google Scholar
[5]Deak, I., An economical method for random number generation and a normal generator, Computing, 27 (1981), 113121.Google Scholar
[6]Gibson, M.A. and Bruck, J., Exact stochastic simulation of chemical systems with many species and many channels, J. Phys. Chem., 105 (2000), 18761889.CrossRefGoogle Scholar
[7]Gilks, W.R., Best, N.G., and Tan, K.K.C., Adaptive rejection metropolis sampling, Applied Statistics, 44 (1995), 455472.Google Scholar
[8]Gilks, W.R. and Wild, P., Adaptive rejection sampling for Gibbs sampling, Applied Statistics, 41 (1992), 337348.Google Scholar
[9]Gillespie, D.T., Stochastic simulation of chemical kinetics, Annu. Rev. Phys. Chem., 58 (2007), 3555.Google Scholar
[10]Gillespie, D.T., A general method for numerically simulating the stochastic time evolution of coupled chemical reactions, J. Comput. Phys., 22 (1976), 403434.CrossRefGoogle Scholar
[11]Marsaglia, G., Tsang, W.W., and Wang, J., Fast generation of discrete random variables, Journal of Statistical Software, 11(3) (2004), 111.Google Scholar
[12]Marsaglia, G. and Tsang, W.W., A fast, easily implemented method for sampling from decreasing or symmetric unimodal density functions, SIAM J. Sci. Stat. Comput., 5(2) (1984), 349359.Google Scholar
[13]Marsaglia, G., Xorshift RNGs, Journal of Statistical Software, 8(14) (2003), 19.CrossRefGoogle Scholar
[14]McCollum, J.M., Peterson, G.D., Cox, C.D., Simpson, M.L., and Samatova, N.F., The sorting direct method for stochastic simulation of biochemical systems with varying reaction execution behavior, Comput. Bio. Chem., 30 (2006), 3949.Google Scholar
[15]Oxenius, J.T., Kinetic Theory of Particles and Photons, Springer-Verlag, Berlin (1986).CrossRefGoogle Scholar
[16]Ramaswamy, R., Gonzalez-Segredo, N., and Sbalzarini, I.F., A new class of highly efficient exact stochastic simulation algorithms for chemical reaction networks, J. Chem. Phys., 130 (2009), 244104.CrossRefGoogle ScholarPubMed
[17]Ramaswamy, R. and Sbalzarini, I.F., A partial-propensity variant of the composition- rejection stochastic simulation algorithm for chemical reaction networks, J. Chem. Phys., 132 (2010), 044102.CrossRefGoogle ScholarPubMed
[18]Ramaswamy, R. and Sbalzarini, I.F., A partial-propensity formulation of the stochastic simulation algorithm for chemical reaction networks with delays, J. Chem. Phys., 134 (2011), 014106.Google Scholar
[19]Vose, M.D., A linear algorithm for generating random numbers with a given distribution, IEEE Transaction and Software Engineering, 17(9) (1991), 972975.Google Scholar
[20]Walker, A.J., An efficient method for generating discrete random variables with general distributions, ACM TOMS, 3 (1977), 253256.Google Scholar
[21]Zeldovich, Y.B. and Raizer, Y.P., Physics of Shock Waves and High-Temperature Hydrodynamic Phenomena, Dover, Mineola, NY (2002).Google Scholar