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Stochastic Optimization: Approximate Bayesian Inference and Complete Expected Improvement
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Stochastic Optimization: Approximate Bayesian Inference and Complete Expected Improvement. |
| 개인저자 | Chen, Ye. |
| 단체저자명 | University of Maryland, College Park. Mathematics. |
| 발행사항 | [S.l.]: University of Maryland, College Park., 2018. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2018. |
| 형태사항 | 150 p. |
| 기본자료 저록 | Dissertation Abstracts International 80-02B(E). Dissertation Abstract International |
| ISBN | 9780438402157 |
| 학위논문주기 | Thesis (Ph.D.)--University of Maryland, College Park, 2018. |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 80-02(E), Section: B.
Advisers: Ilya Ryzhov |
| 요약 | 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 |
| 요약 | 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 |
| 요약 | 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 |
| 일반주제명 | Statistics. Operations research. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
