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020 ▼a 9780438088764
035 ▼a (MiAaPQ)AAI10825193
035 ▼a (MiAaPQ)ucsd:17516
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 248032
0820 ▼a 530
1001 ▼a Shirman, Aleksandra.
24510 ▼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.
5021 ▼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
71020 ▼a University of California, San Diego. ▼b Physics.
7730 ▼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
85640 ▼u http://www.riss.kr/pdu/ddodLink.do?id=T14998740 ▼n KERIS
980 ▼a 201812 ▼f 2019
990 ▼a 관리자