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On the Application, Theory and Compu...

Yu, Jing.

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On the Application, Theory and Computation of Optimal Experimental Design in the Context of Sensor Placement.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	On the Application, Theory and Computation of Optimal Experimental Design in the Context of Sensor Placement./
作者:	Yu, Jing.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
面頁冊數:	159 p.
附註:	Source: Dissertations Abstracts International, Volume: 81-04, Section: B.
Contained By:	Dissertations Abstracts International81-04B.
標題:	Statistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13858427
ISBN:	9781085789691

On the Application, Theory and Computation of Optimal Experimental Design in the Context of Sensor Placement.
Yu, Jing.

On the Application, Theory and Computation of Optimal Experimental Design in the Context of Sensor Placement. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 159 p.

Source: Dissertations Abstracts International, Volume: 81-04, Section: B.

Thesis (Ph.D.)--The University of Chicago, 2019.

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

Optimal experimental designs are a class of experimental designs that are optimal with respect to some statistical criterion. Sensor placement is a sampling decision on data collection which aims to minimize the uncertainty in parameter estimation. This thesis focuses on two fundamental elements: the selection of sensor locations under statistically optimal conditions, and the computation of sensor placement with an efficient algorithm. We first present a design of experiments framework for sensor placement in a natural gas pipeline system where the dynamics are described by partial differential equations, and apply sum-up rounding strategy as a heuristic to determine the sensor locations. We continue to develop convergence theory on sum-up rounding for Bayesian inverse problems, where the direct relationship is described through a discretized integral equation. We show that the integer solution from sum-up rounding is asymptotically optimal in the limit of increasingly refined meshes, for different experimental design criteria (A- and D- optimal), and demonstrate its superior performance in comparison with other standard strategies. We also propose an optimization algorithm to compute the sensor locations, based on sequential quadratic programming and Chebyshev interpolation. By providing gradient and Hessian information on the objective, we solve a sequence of quadratic programs with interior point method and achieves a complexity of O(n logs(n)), while controlling the error through choosing the number of interpolation points to satisfy a user-defined precision level.

ISBN: 9781085789691Subjects--Topical Terms:

517247
Statistics.
Subjects--Index Terms:

Bayesian inverse problem

On the Application, Theory and Computation of Optimal Experimental Design in the Context of Sensor Placement.
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506 $a This item must not be sold to any third party vendors.
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653 $a Integer programming
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