| LDR | | 02077nam u200397 4500 |
| 001 | | 000000422865 |
| 005 | | 20190215170454 |
| 008 | | 181129s2017 |||||||||||||||||c||eng d |
| 020 | |
▼a 9780355773361 |
| 035 | |
▼a (MiAaPQ)AAI10618260 |
| 035 | |
▼a (MiAaPQ)nyu:13064 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 519 |
| 100 | 1 |
▼a Daon, Yair. |
| 245 | 10 |
▼a PDE-Based Prior Distributions and D-Optimal Design in Infinite-Dimensional Bayesian Inverse Problems. |
| 260 | |
▼a [S.l.]:
▼b New York University.,
▼c 2017. |
| 260 | 1 |
▼a Ann Arbor:
▼b ProQuest Dissertations & Theses,
▼c 2017. |
| 300 | |
▼a 113 p. |
| 500 | |
▼a Source: Dissertation Abstracts International, Volume: 79-08(E), Section: B. |
| 500 | |
▼a Adviser: Georg Stadler. |
| 502 | 1 |
▼a Thesis (Ph.D.)--New York University, 2017. |
| 520 | |
▼a 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 |
| 520 | |
▼a 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 |
| 520 | |
▼a 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 |
| 590 | |
▼a School code: 0146. |
| 650 | 4 |
▼a Applied mathematics. |
| 690 | |
▼a 0364 |
| 710 | 20 |
▼a New York University.
▼b Mathematics. |
| 773 | 0 |
▼t Dissertation Abstracts International
▼g 79-08B(E). |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0146 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2017 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T14996637
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a ***1012033 |