Northwestern University

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Asymptotic Uncertainty Quantification and Its Application in Efficient Sampling and Learning

Description:

The ever growing desire for accurate estimation and efficient learning necessitates the efforts to quantitatively characterize uncertainties for models. In this thesis, four problems pertaining to uncertainty quantification are discussed: A sequential stopping framework of constructing fixed-precision confidence regions is proposed for a class of multivariate simulation problems where variance...

Keyword:

Ranking and Selection, Reinforcement Learning, Statistical Learning, Stochastic Gradient Descent, and Uncertainty Quantification

Subject:

Applied mathematics, Statistics, and Computer science

Creator:

Zhu, Yi

Owner:

Scholarly Digital Publishing

Language:

en

Date Uploaded:

01/21/2021

Date Created:

2020-01-01

Resource Type:

Dissertation

Alternate Identifier:

http://dissertations.umi.com/northwestern:15080 and etdadmin_upload_741745