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Adaptive optimal allocation in stratified sampling methods
Pierre Etore,Benjamin Jourdain +1 more
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In this article, a stratified sampling algorithm is proposed in which the random drawings made in the strata to compute the expectation of interest are also used to adaptively modify the proportion of further drawings in each stratum.Abstract:
In this paper, we propose a stratified sampling algorithm in which the random drawings made in the strata to compute the expectation of interest are also used to adaptively modify the proportion of further drawings in each stratum. These proportions converge to the optimal allocation in terms of variance reduction. And our stratified estimator is asymptotically normal with asymptotic variance equal to the minimal one. Numerical experiments confirm the efficiency of our algorithm.read more
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Proceedings Article
Active Stratified Sampling with Clustering-Based Type Systems for Predicting the Search Tree Size of Problems with Real-Valued Heuristics
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Adaptive stratified sampling for structural reliability analysis
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DissertationDOI
Hardware-accelerated algorithms in visual computing
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References
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Book
Monte Carlo Methods in Financial Engineering
TL;DR: This paper presents a meta-modelling procedure that automates the very labor-intensive and therefore time-heavy and therefore expensive and expensive process of manually computing random numbers and random Variables.
Journal ArticleDOI
Asymptotically Optimal Importance Sampling and Stratification for Pricing Path‐Dependent Options
TL;DR: In this article, a variance reduction technique for Monte Carlo simulations of path-dependent options driven by high-dimensional Gaussian vectors is proposed, which combines importance sampling based on a change of drift with stratified sampling along a small number of key dimensions.
Book
Introduction to Optimization
TL;DR: Series Preface*Preface*Introduction*Linear Programming*Nonlinear Programming* Approximation Techniques*Variational Problems and Dynamic Programming*Optimal Control*References*Index
Journal ArticleDOI
Adaptative Monte Carlo Method, A Variance Reduction Technique
TL;DR: An adaptative variance reduction method for Monte Carlo simulations that uses importance sampling scheme based on a change of drift and develops two applications of the procedure for variance reduction in a Monte Carlo computation in finance and in reliability.
Journal ArticleDOI
Controlled stratification for quantile estimation
TL;DR: In this paper, variance reduction techniques for the estimation of quantiles of the output of a complex model with random input parameters are discussed, based on the use of a reduced model, such as a metamodel or a response surface.