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PDE-Based Prior Distributions and D-Optimal Design in Infinite-Dimensional Bayesian Inverse Problems
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | PDE-Based Prior Distributions and D-Optimal Design in Infinite-Dimensional Bayesian Inverse Problems. |
| 개인저자 | Daon, Yair. |
| 단체저자명 | New York University. Mathematics. |
| 발행사항 | [S.l.]: New York University., 2017. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2017. |
| 형태사항 | 113 p. |
| 기본자료 저록 | Dissertation Abstracts International 79-08B(E). Dissertation Abstract International |
| ISBN | 9780355773361 |
| 학위논문주기 | Thesis (Ph.D.)--New York University, 2017. |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 79-08(E), Section: B.
Adviser: Georg Stadler. |
| 요약 | This dissertation describes an investigation into aspects of infinite-dimensional Bayesian inverse problems. In particular, I present methods for generating statistically sound PDE-based Gaussian priors, numerical experiments with these priors o |
| 요약 | In the first part, the task of generating statistically sound priors for infinite-dimensional Bayesian inverse problems is considered. The problem with using PDE-based Gaussian priors is identified as a boundary effect related to the boundary co |
| 요약 | In the second part, the problem of Bayesian design of experiments in infinite dimensions is studied, with the goal of understanding the phenomenon of sensor-clusterization. First, the occurrence of such phenomenon is demonstrated numerically. Th |
| 일반주제명 | Applied mathematics. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
