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020 ▼a 9780438402157
035 ▼a (MiAaPQ)AAI10845135
035 ▼a (MiAaPQ)umd:19352
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 248032
0491 ▼f DP
0820 ▼a 310
1001 ▼a Chen, Ye.
24510 ▼a Stochastic Optimization: Approximate Bayesian Inference and Complete Expected Improvement.
260 ▼a [S.l.] : ▼b University of Maryland, College Park., ▼c 2018
260 1 ▼a Ann Arbor : ▼b ProQuest Dissertations & Theses, ▼c 2018
300 ▼a 150 p.
500 ▼a Source: Dissertation Abstracts International, Volume: 80-02(E), Section: B.
500 ▼a Advisers: Ilya Ryzhov
5021 ▼a Thesis (Ph.D.)--University of Maryland, College Park, 2018.
520 ▼a Stochastic optimization includes modeling, computing and decision making. In practice, due to the limitation of mathematical tools or real budget, many practical solution methods are designed using approximation techniques or taking forms that a
520 ▼a The first part of the thesis is the consistency analysis of sequential learning algorithms under approximate Bayesian inference. Approximate Bayesian inference is a powerful methodology for constructing computationally efficient statistical mech
520 ▼a The second part of the thesis proposes a budget allocation algorithm for the ranking and selection problem. The ranking and selection problem is a well-known mathematical framework for the formal study of optimal information collection. Expected
590 ▼a School code: 0117.
650 4 ▼a Statistics.
650 4 ▼a Operations research.
690 ▼a 0463
690 ▼a 0796
71020 ▼a University of Maryland, College Park. ▼b Mathematics.
7730 ▼t Dissertation Abstracts International ▼g 80-02B(E).
773 ▼t Dissertation Abstract International
790 ▼a 0117
791 ▼a Ph.D.
792 ▼a 2018
793 ▼a English
85640 ▼u http://www.riss.kr/pdu/ddodLink.do?id=T15000042 ▼n KERIS
980 ▼a 201812 ▼f 2019
990 ▼a 관리자 ▼b 관리자