Canonical Moments for Optimal Uncertainty Quantification on a Variety

被引:0
|
作者
Stenger, Jerome [1 ,2 ]
Gamboa, Fabrice [1 ]
Keller, Merlin [2 ]
Iooss, Bertrand [1 ,2 ]
机构
[1] Inst Math Toulouse, 118 Route Narbonne, Toulouse, France
[2] EDF R&D, 6 Quai Watier, Chatou, France
来源
关键词
Canonical moments; Optimal uncertainty quantification; Robustness;
D O I
10.1007/978-3-030-26980-7_59
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The purpose of this work is to optimize an affine functional over positive measures. More precisely, we deal with a probability of failure (P.O.F). The optimization is realized over a set of distributions satisfying moment constraints, called moment set. The optimum is to be found on an extreme point of this moment set. Winkler's classification of those extreme points states they are finite discrete measures. The set of the support points of all discrete measures in the moment set is a manifold over which the P.O.F is optimized. We characterize the manifold's structure by proving it is an algebraic variety. It is the zero locus of polynomials defined thanks to the canonical moments. This reduces a highly constrained optimization over the moment set onto a constraint free manifold.
引用
下载
收藏
页码:571 / 578
页数:8
相关论文
共 50 条
  • [31] Canonical Moments and Random Spectral Measures
    Gamboa, F.
    Rouault, A.
    JOURNAL OF THEORETICAL PROBABILITY, 2010, 23 (04) : 1015 - 1038
  • [32] Canonical Moments and Random Spectral Measures
    F. Gamboa
    A. Rouault
    Journal of Theoretical Probability, 2010, 23 : 1015 - 1038
  • [33] Uncertainty quantification
    Leitch, Matthew
    JOURNAL OF RISK FINANCE, 2005, 6 (01)
  • [34] Optimal sampling-based neural networks for uncertainty quantification and stochastic optimization
    Gupta, Subham
    Paudel, Achyut
    Thapa, Mishal
    Mulani, Sameer B.
    Walters, Robert W.
    AEROSPACE SCIENCE AND TECHNOLOGY, 2023, 133
  • [35] Output-Weighted Optimal Sampling for Bayesian Experimental Design and Uncertainty Quantification
    Blanchard, Antoine
    Sapsis, Themistoklis
    SIAM-ASA JOURNAL ON UNCERTAINTY QUANTIFICATION, 2021, 9 (02): : 564 - 592
  • [36] A New Signal Recovery Method Based on Optimal Uncertainty Quantification in Compressed Sensing
    Li, Ming
    Wen, Chenglin
    2015 SIXTH INTERNATIONAL CONFERENCE ON INTELLIGENT CONTROL AND INFORMATION PROCESSING (ICICIP), 2015, : 438 - 442
  • [37] Uncertainty Quantification of NEPTUNE_CFD calculation by Optimal Statistical Estimator Method
    Prosek, Andrej
    Koncar, Bostjan
    Leskovar, Matjaz
    24TH INTERNATIONAL CONFERENCE NUCLEAR ENERGY FOR NEW EUROPE, (NENE 2015), 2015,
  • [38] Note on the invariants of the canonical system of an algebraic variety
    Todd, JA
    Maxwell, EA
    PROCEEDINGS OF THE CAMBRIDGE PHILOSOPHICAL SOCIETY, 1937, 33 : 438 - 443
  • [39] Kinetic moments method for the canonical ensemble distribution
    Hoover, WG
    Holian, BL
    PHYSICS LETTERS A, 1996, 211 (05) : 253 - 257
  • [40] Erratum to: Canonical Moments and Random Spectral Measures
    F. Gamboa
    A. Rouault
    Journal of Theoretical Probability, 2017, 30 : 701 - 702