Efficient step size selection for the tau-leaping simulation method.

654 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Simulating the evolution of soot mixing state with a particle-resolved aerosol model (2008, methodology): “Gillespie [2001] developed the tau-leaping method for efficient generation of many events with near-constant rates, with extensions by Gillespie and Petzold [2003], Rathinam et al. [2003], Cao et al. [2006] and others, including for multiscale systems with scale separation by Cao et al. [2005].”
Stochastic simulation of chemical kinetics. (2007, background): “... that would give us the probability density function of X(t) insteadofarandomsampleofX(t).ThekeytogeneratingsimulatedtrajectoriesofX(t) is not the function P(x,t |x0,t0), but rather a new probability function p(% , j |x,t), which is defined as follows: p(% , j |x,t)d% # = the probability, given X”
Networks and the Epidemiology of Infectious Disease (2010, background): “However, the ability to allow τ to vary during a simulation to account for the processes involved [112] has potential benefits.”
Stochastic switching in biology: from genotype to phenotype (2017, methodology): “This issue has been addressed in various modifications of the tau-leaping procedure, see for example Cao et al [60].”