Towards automatic global error control: Computable weak error expansion for the tau-leap method

Peer Jesper Karlsson, Raul Tempone

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

This work develops novel error expansions with computable leading order terms for the global weak error in the tau-leap discretization of pure jump processes arising in kinetic Monte Carlo models. Accurate computable a posteriori error approximations are the basis for adaptive algorithms, a fundamental tool for numerical simulation of both deterministic and stochastic dynamical systems. These pure jump processes are simulated either by the tau-leap method, or by exact simulation, also referred to as dynamic Monte Carlo, the Gillespie Algorithm or the Stochastic Simulation Slgorithm. Two types of estimates are presented: an a priori estimate for the relative error that gives a comparison between the work for the two methods depending on the propensity regime, and an a posteriori estimate with computable leading order term. © de Gruyter 2011.
Original languageEnglish (US)
Pages (from-to)233-278
Number of pages46
JournalMonte Carlo Methods and Applications
Volume17
Issue number3
DOIs
StatePublished - Jan 2011

ASJC Scopus subject areas

  • Statistics and Probability
  • Applied Mathematics

Fingerprint Dive into the research topics of 'Towards automatic global error control: Computable weak error expansion for the tau-leap method'. Together they form a unique fingerprint.

Cite this