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Combinatorial Inference for Large-Scale Data Analysis
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Combinatorial Inference for Large-Scale Data Analysis. |
| 개인저자 | Lu, Junwei. |
| 단체저자명 | Princeton University. Operations Research and Financial Engineering. |
| 발행사항 | [S.l.]: Princeton University., 2018. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2018. |
| 형태사항 | 237 p. |
| 기본자료 저록 | Dissertation Abstracts International 79-10B(E). Dissertation Abstract International |
| ISBN | 9780438047709 |
| 학위논문주기 | Thesis (Ph.D.)--Princeton University, 2018. |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.
Advisers: Han Liu |
| 요약 | Problems of inferring the combinatorial structures of networks arise in many real applications ranging from genomic regulatory networks, brain networks to social networks. This poses new and challenging problems on the uncertainty assessment and |
| 요약 | In the first part of the thesis, we propose a unified inferential method to test hypotheses on the global combinatorial properties of graphical models. We showed that my method works for general monotone graph properties that can be preserved un |
| 요약 | In the second part of the thesis, we generalize the combinatorial inference for larger family of graphical models. We propose a novel class of dynamic nonparanormal graphical models, which allows us to model high dimensional heavy-tailed systems |
| 일반주제명 | Statistics. Operations research. Artificial intelligence. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
