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Bayesian Modeling Strategies for Complex Data Structures, with Applications to Neuroscience and Medicine
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Bayesian Modeling Strategies for Complex Data Structures, with Applications to Neuroscience and Medicine. |
| 개인저자 | Lu, Feihan. |
| 단체저자명 | Columbia University. Statistics. |
| 발행사항 | [S.l.]: Columbia University., 2018. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2018. |
| 형태사항 | 142 p. |
| 기본자료 저록 | Dissertation Abstracts International 79-11B(E). Dissertation Abstract International |
| ISBN | 9780438124042 |
| 학위논문주기 | Thesis (Ph.D.)--Columbia University, 2018. |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 79-11(E), Section: B.
Advisers: Michael E. Sobel |
| 요약 | Bayesian statistical procedures use probabilistic models and probability distributions to summarize data, estimate unknown quantities of interest, and predict future observations. The procedures borrow strength from other observations in the dat |
| 일반주제명 | Statistics. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
