BEGIN:VCALENDAR VERSION:2.0 PRODID:-//Talks.cam//talks.cam.ac.uk// X-WR-CALNAME:Talks.cam BEGIN:VEVENT SUMMARY:Efficiency of Stochastic Simulations - Des Higham () DTSTART:20160118T153000Z DTEND:20160118T161500Z UID:TALK64644@talks.cam.ac.uk CONTACT:INI IT DESCRIPTION:Co-authors: David F. Anderson (University of Wisconsin-Madiso n)\, Yu Sun (University of Wisconsin-Madison)

I will analyz e and compare the computational complexity of different simulation strateg ies for continuous time Markov chains. I consider the task of approximatin g the expected value of some functional of the state of the system over a compact time interval. This task is a bottleneck in many large-scale compu tations arising in biochemical kinetics and cell biology. In this context\ , the terms '\;Gillespie'\;s method'\;\, '\;The Stochastic Sim ulation Algorithm'\; and '\;The Next Reaction Method'\; are widel y used to describe exact simulation methods. For example\, Google Scholar records more than 6\,000 citations to Gillespie'\;s seminal 1977 paper. I will look at the use of standard Monte Carlo when samples are produced by exact simulation and by approximation with tau-leaping or an Euler-Maru yama discretization of a diffusion approximation. In particular\, I will p oint out some possible pitfalls when computational complexity is analysed. Appropriate modifications o f recently proposed multilevel Monte Carlo al gorithms will then be studied for the tau-leaping and Euler-Maruyama appro aches. I will pay particular attention to a parameterization of the proble m that\, in the mass action chemical kinetics setting\, corresponds to the classical system size scaling.

Related Links LOCATION:Seminar Room 1\, Newton Institute END:VEVENT END:VCALENDAR