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Asymptotic Uncertainty Quantificatio...

Zhu, Yi.

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Asymptotic Uncertainty Quantification and Its Application in Efficient Sampling and Learning.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Asymptotic Uncertainty Quantification and Its Application in Efficient Sampling and Learning./
Author:	Zhu, Yi.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
Description:	197 p.
Notes:	Source: Dissertations Abstracts International, Volume: 82-01, Section: B.
Contained By:	Dissertations Abstracts International82-01B.
Subject:	Statistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27964080
ISBN:	9798662367371

Asymptotic Uncertainty Quantification and Its Application in Efficient Sampling and Learning.
Zhu, Yi.

Asymptotic Uncertainty Quantification and Its Application in Efficient Sampling and Learning. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 197 p.

Source: Dissertations Abstracts International, Volume: 82-01, Section: B.

Thesis (Ph.D.)--Northwestern University, 2020.

This item must not be sold to any third party vendors.

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 estimation is difficult.An algorithm is developed to construct asymptotically valid confidence regions for model parameters for Stochastic Gradient Descent using the batch means method. Statistical inference for reinforcement learning is studied and the statistical property can be applied to develop efficient exploration policies.Uncertainty of decision making is discussed under three asymptotic regimes for ranking and selection (best arm identification) problems with general sample distributions.

ISBN: 9798662367371Subjects--Topical Terms:

517247
Statistics.
Subjects--Index Terms:

Ranking and selection

Asymptotic Uncertainty Quantification and Its Application in Efficient Sampling and Learning.
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245 1 0 $a Asymptotic Uncertainty Quantification and Its Application in Efficient Sampling and Learning.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2020
300 $a 197 p.
500 $a Source: Dissertations Abstracts International, Volume: 82-01, Section: B.
500 $a Advisor: Nelson, Barry;Dong, Jing.
502 $a Thesis (Ph.D.)--Northwestern University, 2020.
506 $a This item must not be sold to any third party vendors.
520 $a 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 estimation is difficult.An algorithm is developed to construct asymptotically valid confidence regions for model parameters for Stochastic Gradient Descent using the batch means method. Statistical inference for reinforcement learning is studied and the statistical property can be applied to develop efficient exploration policies.Uncertainty of decision making is discussed under three asymptotic regimes for ranking and selection (best arm identification) problems with general sample distributions.
590 $a School code: 0163.
650 4 $a Statistics. $3 517247
650 4 $a Applied mathematics. $3 2122814
650 4 $a Computer science. $3 523869
653 $a Ranking and selection
653 $a Reinforcement learning
653 $a Statistical learning
653 $a Stochastic gradient descent
653 $a Uncertainty quantification
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710 2 $a Northwestern University. $b Industrial Engineering and Management Sciences. $3 1023502
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856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27964080