| LDR | | 01745nmm uu200385 4500 |
| 001 | | 000000331914 |
| 005 | | 20240805165630 |
| 008 | | 181129s2018 |||||||||||||||||c||eng d |
| 020 | |
▼a 9780438291423 |
| 035 | |
▼a (MiAaPQ)AAI10826550 |
| 035 | |
▼a (MiAaPQ)ucdavis:17988 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 248032 |
| 082 | 0 |
▼a 310 |
| 100 | 1 |
▼a Wang, Nana. |
| 245 | 10 |
▼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. |
| 502 | 1 |
▼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 |
| 710 | 20 |
▼a University of California, Davis.
▼b Statistics. |
| 773 | 0 |
▼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 |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T14998903
▼n KERIS |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a 관리자 |