| LDR | | 02235nam u200445 4500 |
| 001 | | 000000422004 |
| 005 | | 20190215165758 |
| 008 | | 181129s2018 |||||||||||||||||c||eng d |
| 020 | |
▼a 9780438088764 |
| 035 | |
▼a (MiAaPQ)AAI10825193 |
| 035 | |
▼a (MiAaPQ)ucsd:17516 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 530 |
| 100 | 1 |
▼a Shirman, Aleksandra. |
| 245 | 10 |
▼a Strategic Monte Carlo and Variational Methods in Statistical Data Assimilation for Nonlinear Dynamical Systems. |
| 260 | |
▼a [S.l.]:
▼b University of California, San Diego.,
▼c 2018. |
| 260 | 1 |
▼a Ann Arbor:
▼b ProQuest Dissertations & Theses,
▼c 2018. |
| 300 | |
▼a 102 p. |
| 500 | |
▼a Source: Dissertation Abstracts International, Volume: 79-11(E), Section: B. |
| 500 | |
▼a Adviser: Henry D. I. Abarbanel. |
| 502 | 1 |
▼a Thesis (Ph.D.)--University of California, San Diego, 2018. |
| 520 | |
▼a Data Assimilation (DA) is a method through which information is extracted from measured quantities and with the help of a mathematical model is transferred through a probability distribution to unknown or unmeasured states and parameters charact |
| 520 | |
▼a Many recent DA efforts rely on an probability distribution optimization that locates the most probable state and parameter values given a set of data. The procedure developed and demonstrated here extends the optimization by appending a biased r |
| 520 | |
▼a This thesis will conclude with an exploration of the equivalence of machine learning and the formulation of statistical DA. The application of previous DA methods are demonstrated on the classic machine learning problem: the characterization of |
| 590 | |
▼a School code: 0033. |
| 650 | 4 |
▼a Physics. |
| 650 | 4 |
▼a Statistics. |
| 650 | 4 |
▼a Biophysics. |
| 690 | |
▼a 0605 |
| 690 | |
▼a 0463 |
| 690 | |
▼a 0786 |
| 710 | 20 |
▼a University of California, San Diego.
▼b Physics. |
| 773 | 0 |
▼t Dissertation Abstracts International
▼g 79-11B(E). |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0033 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2018 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T14998740
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a ***1012033 |