LDR | | 02038nmm uu200445 4500 |
001 | | 000000334082 |
005 | | 20240805175110 |
008 | | 181129s2018 |||||||||||||||||c||eng d |
020 | |
▼a 9780438304321 |
035 | |
▼a (MiAaPQ)AAI10828009 |
035 | |
▼a (MiAaPQ)uci:15212 |
040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 248032 |
082 | 0 |
▼a 310 |
100 | 1 |
▼a Holbrook, Andrew J. |
245 | 10 |
▼a Geometric Bayes. |
260 | |
▼a [S.l.] :
▼b University of California, Irvine.,
▼c 2018 |
260 | 1 |
▼a Ann Arbor :
▼b ProQuest Dissertations & Theses,
▼c 2018 |
300 | |
▼a 207 p. |
500 | |
▼a Source: Dissertation Abstracts International, Volume: 80-01(E), Section: B. |
500 | |
▼a Adviser: Babak Shahbaba. |
502 | 1 |
▼a Thesis (Ph.D.)--University of California, Irvine, 2018. |
520 | |
▼a This dissertation is an investigation into the intersections between differential geometry and Bayesian analysis. The former is the mathematical discipline that underlies our understanding of the spatial structure of the universe |
520 | |
▼a A major component of this work is the development and application of probabilistic models defined over smooth manifolds: dependencies between time series are modeled using the manifold of Hermitian positive definite matrices |
520 | |
▼a This dissertation is ordered as follows. In Chapter 1, the general setting is introduced along with the rudiments of Riemannian geometry. In Chapter 2, the geodesic Lagrangian Monte Carlo algorithm is presented and used for Bayesian inference ov |
590 | |
▼a School code: 0030. |
650 | 4 |
▼a Statistics. |
650 | 4 |
▼a Applied mathematics. |
650 | 4 |
▼a Computer science. |
690 | |
▼a 0463 |
690 | |
▼a 0364 |
690 | |
▼a 0984 |
710 | 20 |
▼a University of California, Irvine.
▼b Statistics - Ph.D.. |
773 | 0 |
▼t Dissertation Abstracts International
▼g 80-01B(E). |
773 | |
▼t Dissertation Abstract International |
790 | |
▼a 0030 |
791 | |
▼a Ph.D. |
792 | |
▼a 2018 |
793 | |
▼a English |
856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T14999100
▼n KERIS |
980 | |
▼a 201812
▼f 2019 |
990 | |
▼a 관리자 |