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Scalable Bayesian Network Learning and Its Applications
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Scalable Bayesian Network Learning and Its Applications. |
| 개인저자 | Basak, Aniruddha. |
| 단체저자명 | Carnegie Mellon University. Electrical and Computer Engineering. |
| 발행사항 | [S.l.]: Carnegie Mellon University., 2018. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2018. |
| 형태사항 | 116 p. |
| 기본자료 저록 | Dissertation Abstracts International 80-01B(E). Dissertation Abstract International |
| ISBN | 9780438326347 |
| 학위논문주기 | Thesis (Ph.D.)--Carnegie Mellon University, 2018. |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 80-01(E), Section: B.
Adviser: Ole J. Mengshoel. |
| 요약 | The Bayesian network is a powerful tool for modeling of cause-effect and other uncertain relations between variables in a domain of interest. Probabilistic reasoning with a Bayesian network offers prediction of one or more unobserved variables o |
| 요약 | This research develops scalable techniques for both structure learning and parameter learning of Bayesian networks from data. For the parameter learning task, we proposed a novel decomposition of the Expectation Maximization algorithm in the Map |
| 요약 | For the Bayesian network structure learning task, a novel score-based method is developed. Score-based structure learning may seems inherently sequential, due to its use of iterative improvement steps. However, we bring parallelism to the score- |
| 요약 | We apply the proposed techniques to several datasets including two real-world engineering problems: smart building optimization and next-generation air traffic control. For smart building optimization, we study the isolation of candidate causes |
| 일반주제명 | Computer engineering. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
