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JOURNALS // Zhurnal Vychislitel'noi Matematiki i Matematicheskoi Fiziki // Archive

Zh. Vychisl. Mat. Mat. Fiz., 2014 Volume 54, Number 2, Pages 183–194 (Mi zvmmf9986)

This article is cited in 1 paper

Variance reduction techniques for estimation of integrals over a set of branching trajectories

E. A. Tsvetkov

Moscow Institute of Physics and Technology (State University), Institutskii per. 9, Dolgoprudnyi, Moscow oblast, 141700, Russia

Abstract: Monte Carlo variance reduction techniques within the supertrack approach are justified as applied to estimating non-Boltzmann tallies equal to the mean of a random variable defined on the set of all branching trajectories. For this purpose, a probability space is constructed on the set of all branching trajectories, and the unbiasedness of this method is proved by averaging over all trajectories. Variance reduction techniques, such as importance sampling, splitting, and Russian roulette, are discussed. A method is described for extending available codes based on the von Neumann-Ulam scheme in order to cover the supertrack approach.

Key words: statistical modeling, variance reduction techniques, supertrack, branching trajectories, non-Boltzmann tallies.

UDC: 519.676

Received: 08.08.2012
Revised: 10.10.2012

DOI: 10.7868/S0044466914020148


 English version:
Computational Mathematics and Mathematical Physics, 2014, 54:2, 195–205

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