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Asymptotic Uncertainty Quantificatio...

Zhu, Yi.

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

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Asymptotic Uncertainty Quantification and Its Application in Efficient Sampling and Learning./
作者:	Zhu, Yi.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
面頁冊數:	197 p.
附註:	Source: Dissertations Abstracts International, Volume: 82-01, Section: B.
Contained By:	Dissertations Abstracts International82-01B.
標題:	Statistics. -
電子資源:	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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