Uncertainty Quantification for Stochastic Approximation Limits Using Chaos Expansion
DOI10.1137/18M1178517zbMath1448.62125MaRDI QIDQ5119639
Emmanuel Gobet, Uladzislau Stazhynski, Stéphane Crépey, Gersende Fort
Publication date: 31 August 2020
Published in: SIAM/ASA Journal on Uncertainty Quantification (Search for Journal in Brave)
chaos expansionuncertainty quantificationalmost-sure convergencestochastic approximation in Hilbert space
Stochastic programming (90C15) Strong limit theorems (60F15) Stochastic approximation (62L20) Approximation by polynomials (41A10) Rate of convergence, degree of approximation (41A25) Limit theorems for vector-valued random variables (infinite-dimensional case) (60B12)
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Nonparametric stochastic approximation with large step-sizes
- An invariance principle for a finite dimensional stochastic approximation method in a Hilbert space
- On H-valued Robbins-Monro processes
- Finite dimensional approximation and Newton-based algorithm for stochastic approximation in Hilbert space
- Géza Freud, orthogonal polynomials and Christoffel functions. A case study
- Minimizing noisy functionals in Hilbert space: An extension of the Kiefer-Wolfowitz procedure
- Abstract stochastic approximations and applications
- Quasi-regression
- Asymptotic Properties of Some Projection-based Robbins-Monro Procedures in a Hilbert Space
- Spectral Methods for Uncertainty Quantification
- Lectures on Stochastic Programming
- Computing VaR and CVaR using stochastic approximation and adaptive unconstrained importance sampling
- Random field finite elements
- Acceleration of Stochastic Approximation by Averaging
- On h–valued stochastic approximation: finite dimenstional projections
- An invariance principle for the Robbins-Monro process in a Hilbert space
- Galerkin Finite Element Approximations of Stochastic Elliptic Partial Differential Equations
- Stochastic approximation schemes for economic capital and risk margin computations
- Functional Analysis
- Spectral Methods
- The Homogeneous Chaos
- Stochastic Estimation of the Maximum of a Regression Function
- A Stochastic Approximation Method
This page was built for publication: Uncertainty Quantification for Stochastic Approximation Limits Using Chaos Expansion
