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Batching Adaptive Variance Reduction
ACM Transactions on Modeling and Computer Simulation, 2023Adaptive Monte Carlo variance reduction is an effective framework for running a Monte Carlo simulation along with a parameter search algorithm for variance reduction, whereas an initialization step is required for preparing problem parameters in some instances.
Chenxiao Song, Reiichiro Kawai
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1988
In this chapter, we discuss various techniques which may be used to make calculations more efficient. In some cases, these techniques require that no further approximations be made to the transport physics. In other cases, the gains in computing speed come at the cost of computing results which may be less accurate since approximations are introduced ...
Bielajew, A. F., Rogers, D. W. O.
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In this chapter, we discuss various techniques which may be used to make calculations more efficient. In some cases, these techniques require that no further approximations be made to the transport physics. In other cases, the gains in computing speed come at the cost of computing results which may be less accurate since approximations are introduced ...
Bielajew, A. F., Rogers, D. W. O.
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Variance reduction and nonnormality
Biometrika, 1974There are many available variance reduction methods and these are described in the sampling theory literature in such works as Kish (1965) and Raj (1968), and in the literature of Monte Carlo methods (Hammersley & Handscomb, 1964) and in the survey paper by Halton (1970).
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Variance Reduction for Simulated Diffusions
SIAM Journal on Applied Mathematics, 1994Summary: This article develops some variance reduction techniques for the Monte- Carlo integration of functionals of the solutions of Itô stochastic differential equations (sdes). The Monte-Carlo method for sdes offers a means of calculating solutions to certain types of parabolic partial differential equations and so has applications in various fields
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Variance Reduction in Smoothing Splines
Scandinavian Journal of Statistics, 2009Abstract. We develop a variance reduction method for smoothing splines. For a given point of estimation, we define a variance‐reduced spline estimate as a linear combination of classical spline estimates at three nearby points. We first develop a variance reduction method for spline estimators in univariate regression models.
Paige, Robert L., Sun, Shan, Wang, Keyi
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Variance reduction in meta-EDA
Proceedings of the 9th annual conference on Genetic and evolutionary computation, 2007We study the benefit of measurement replication when using the Relevance Estimation and Value Calibration method to calibrate a genetic algorithm. We find that replication is not essential to REVAC, which makes it a strong alternative to existing statistical tools which are computationally costly.
Volker Nannen, A. E. Eiben
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Integrated Variance Reduction Strategies for Simulation
Operations Research, 1996We develop strategies for integrated use of certain well-known variance reduction techniques to estimate a mean response in a finite-horizon simulation experiment. The building blocks for these integrated variance reduction strategies are the techniques of conditional expectation, correlation induction (including antithetic variates and Latin ...
Athanassios N. Avramidis +1 more
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Variance reduction for simulation practitioners
Proceedings of the 19th conference on Winter simulation - WSC '87, 1987A comprehensive guide to applying three well-known variance reduction techniques is given, including point and interval estimators, software requirements, and guidelines for experiment design.
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2018
Variance reduction technique plays a crucial role in Monte Carlo methods, as they can significantly reduce the uncertainty inherited in Monte Carlo. While in the previous chapter we already have seen some basic variance reduction methods, here we present more advanced approaches.
Denis Belomestny, John Schoenmakers
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Variance reduction technique plays a crucial role in Monte Carlo methods, as they can significantly reduce the uncertainty inherited in Monte Carlo. While in the previous chapter we already have seen some basic variance reduction methods, here we present more advanced approaches.
Denis Belomestny, John Schoenmakers
openaire +1 more source