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Bayesian designs for sequential lear...

Xie, Jing.

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Bayesian designs for sequential learning problems.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Bayesian designs for sequential learning problems./
Author:	Xie, Jing.
Description:	196 p.
Notes:	Source: Dissertation Abstracts International, Volume: 75-11(E), Section: B.
Contained By:	Dissertation Abstracts International75-11B(E).
Subject:	Operations Research. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3583218
ISBN:	9781321126723

Bayesian designs for sequential learning problems.
Xie, Jing.

Bayesian designs for sequential learning problems. - 196 p.

Source: Dissertation Abstracts International, Volume: 75-11(E), Section: B.

Thesis (Ph.D.)--Cornell University, 2014.

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

We consider the Bayesian formulation of a number of learning problems, where we focus on sequential sampling procedures for allocating simulation effort efficiently.

ISBN: 9781321126723Subjects--Topical Terms:

626629
Operations Research.

Bayesian designs for sequential learning problems.
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020 $a 9781321126723
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100 1 $a Xie, Jing. $3 621655
245 1 0 $a Bayesian designs for sequential learning problems.
300 $a 196 p.
500 $a Source: Dissertation Abstracts International, Volume: 75-11(E), Section: B.
500 $a Adviser: Peter I. Frazier.
502 $a Thesis (Ph.D.)--Cornell University, 2014.
506 $a This item must not be sold to any third party vendors.
506 $a This item must not be added to any third party search indexes.
520 $a We consider the Bayesian formulation of a number of learning problems, where we focus on sequential sampling procedures for allocating simulation effort efficiently.
520 $a We derive Bayes-optimal policies for the problem of multiple comparisons with a known standard, showing that they can be computed efficiently when sampling is limited by probabilistic termination or sampling costs. We provide a tractable method for computing upper bounds on the Bayes-optimal value of a ranking and selection problem, which enables evaluation of optimality gaps for existing ranking and selection procedures. Applying techniques from optimal stopping, multi-armed bandits and Lagrangian relaxation, we are able to efficiently solve the corresponding dynamic programs.
520 $a We develop a new value-of-information-based procedure for the problem of Bayesian optimization via simulation, which incorporates both correlated prior beliefs and correlated sampling distributions. We also introduce a sequential Bayesian algorithm for optimization of expensive functions under low-dimensional input uncertainties. These implementations take advantage of machine learning tools that enable exploring combinatorially large solution spaces, or estimating expectations of simulation output variables with random inputs.
520 $a We present theoretical results characterizing the proposed procedures, compare them numerically against previously developed or standard benchmarking procedures, and apply them to applications in emergency services, manufacturing, and health care.
590 $a School code: 0058.
650 4 $a Operations Research. $3 626629
650 4 $a Statistics. $3 517247
690 $a 0796
690 $a 0463
710 2 $a Cornell University. $b Operations Research and Information Engineering. $3 3169951
773 0 $t Dissertation Abstracts International $g 75-11B(E).
790 $a 0058
791 $a Ph.D.
792 $a 2014
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3583218