Optimizing Adaptive Importance Sampling by Stochastic Approximation
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Publication:4584930
DOI10.1137/18M1173472zbMath1396.65009OpenAlexW2889353161WikidataQ129320164 ScholiaQ129320164MaRDI QIDQ4584930
Publication date: 5 September 2018
Published in: SIAM Journal on Scientific Computing (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1137/18m1173472
importance samplingstochastic approximationuniform distributionvariance reductionsample average approximation
Monte Carlo methods (65C05) Computational aspects related to convexity (52B55) Stochastic approximation (62L20) Stochastic learning and adaptive control (93E35)
Related Items (6)
Dynamic Finite-Budget Allocation of Stratified Sampling with Adaptive Variance Reduction by Strata ⋮ MONTE CARLO VARIANCE REDUCTION METHODS WITH APPLICATIONS IN STRUCTURAL RELIABILITY ANALYSIS ⋮ Batching Adaptive Variance Reduction ⋮ Sampling and change of measure for Monte Carlo integration on simplices ⋮ Adaptive importance sampling and control variates ⋮ Convergence rates for optimised adaptive importance samplers
Cites Work
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- A general control variate method for option pricing under Lévy processes
- Adaptive importance sampling Monte Carlo simulation for general multivariate probability laws
- Minimizing finite sums with the stochastic average gradient
- Adaptive Monte Carlo variance reduction for Lévy processes with two-time-scale stochastic approximation
- Unconstrained recursive importance sampling
- Stochastic approximation with two time scales
- On sample average approximation algorithms for determining the optimal importance sampling parameters in pricing financial derivatives on Lévy processes
- Lectures on Stochastic Programming
- Robust Stochastic Approximation Approach to Stochastic Programming
- Acceleration of Stochastic Approximation by Averaging
- Rate of Convergence for Constrained Stochastic Approximation Algorithms
- Variance Reduction for Asian Options under a General Model Framework*
- Asymptotically optimal allocation of stratified sampling with adaptive variance reduction by strata
- General results on the convergence of stochastic algorithms
- Adaptative Monte Carlo Method, A Variance Reduction Technique
- Optimal Importance Sampling Parameter Search for Lévy Processes via Stochastic Approximation
- Acceleration on Adaptive Importance Sampling with Sample Average Approximation
- Stochastic Dual Coordinate Ascent Methods for Regularized Loss Minimization
- Adaptive Monte Carlo Variance Reduction with Two-time-scale Stochastic Approximation
- An importance sampling method based on the density transformation of Lévy processes
- A Stochastic Approximation Method
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