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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

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