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020 ▼a 9780438291423
035 ▼a (MiAaPQ)AAI10826550
035 ▼a (MiAaPQ)ucdavis:17988
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 248032
0491 ▼f DP
0820 ▼a 310
1001 ▼a Wang, Nana.
24510 ▼a Analysing Dependence in Stochastic Networks via Gaussian Graphical Models.
260 ▼a [S.l.] : ▼b University of California, Davis., ▼c 2018
260 1 ▼a Ann Arbor : ▼b ProQuest Dissertations & Theses, ▼c 2018
300 ▼a 164 p.
500 ▼a Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
500 ▼a Adviser: Wolfgang Polonik.
5021 ▼a Thesis (Ph.D.)--University of California, Davis, 2018.
520 ▼a The topic of this work is modeling and analyzing dependence in stochastic social networks. In a latent variable block model, we present an approach for analyzing the dependence between blocks via the analysis of a latent graphical model by using
520 ▼a The second part of this work extends the iid setting of the first part to a dependent setup. This leads to the analysis of latent dynamic graphical models under uncertainty. Here the concept of locally stationary VAR(p) graphical models comes in
590 ▼a School code: 0029.
650 4 ▼a Statistics.
690 ▼a 0463
71020 ▼a University of California, Davis. ▼b Statistics.
7730 ▼t Dissertation Abstracts International ▼g 79-12B(E).
773 ▼t Dissertation Abstract International
790 ▼a 0029
791 ▼a Ph.D.
792 ▼a 2018
793 ▼a English
85640 ▼u http://www.riss.kr/pdu/ddodLink.do?id=T14998903 ▼n KERIS
980 ▼a 201812 ▼f 2019
990 ▼a 관리자 ▼b 관리자