Approximate zero-variance simulation

  • Authors:

  • Pierre L'Ecuyer;Bruno Tuffin

  • Affiliations:

  • Université de Montreal, Montréal (Québec), Canada;INRIA Rennes Bretagne Atlantique, Campus de Beaulieu, Rennes Cedex, France

  • Venue:
  • Proceedings of the 40th Conference on Winter Simulation
  • Year:
  • 2008

Quantified Score

Hi-index 0.01

Visualization

Abstract

Monte Carlo simulation applies to a wide range of estimation problems, but converges rather slowly in general. Variance reduction techniques can lower the estimation error, sometimes by a large factor, but rarely change the convergence rate of the estimation error. This error usually decreases as the inverse square root of the computational effort, as dictated by the central limit theorem. In theory, there exist simulation estimators with zero variance, i.e., that always provide the exact value. The catch is that these estimators are usually much too difficult (or virtually impossible) to implement. However, there are situations, especially in the context of rare-event simulation, where the zero-variance simulation can be approximated well enough to provide huge efficiency gains. Adaptive versions can even yield a faster convergence rate, including exponential convergence in some cases. This paper gives a brief overview of these methods and discuss their practicality.