Efficient Simulation Methods for Large Stochastic Processes
Hans Plesser (Mathematical Biosciences Institute, The Ohio State University)
(October 24, 2002 9:30 AM - 10:30 AM)
Reaction-diffusion systems, complex biochemical reaction chains, population dynamics, and many more natural phenomena are stochastic processes in which many different events can occur at any time. The simulation of such systems is a formidable task, and I will present some algorithms that allow for the efficient simulation of large systems of stochastic processes.
Briefly, the idea is to use Gillespie's algorithm to determine the time interval between any two events, and then use logarithmic classification of possible events to determine efficiently which event occurs at any one time step. Utilizing proper data structures, this classification and selection scheme can be implemented in a highly time-efficient manner (Fricke and Wendt, 1995).
I will give an introduction into the ideas underlying the logarithmic classification algorithm and give a brief tutorial on how to use the logarithmic class library in simulation code.
Preprint versions of papers explaining the algorithms, source code implementing logarithmic and discrete classes, and a sample program simulating a Lotka-Volterra-style model, is available as the Markov Classes Package