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This article is cited in 1 scientific paper (total 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.
Received: 08.08.2012 Revised: 10.10.2012
Citation:
E. A. Tsvetkov, “Variance reduction techniques for estimation of integrals over a set of branching trajectories”, Zh. Vychisl. Mat. Mat. Fiz., 54:2 (2014), 183–194; Comput. Math. Math. Phys., 54:2 (2014), 195–205
Linking options:
https://www.mathnet.ru/eng/zvmmf9986 https://www.mathnet.ru/eng/zvmmf/v54/i2/p183
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Abstract page: | 260 | Full-text PDF : | 131 | References: | 63 | First page: | 6 |
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