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020 ▼a 9780438326347
035 ▼a (MiAaPQ)AAI10686385
035 ▼a (MiAaPQ)cmu:10183
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 248032
0491 ▼f DP
0820 ▼a 621.3
1001 ▼a Basak, Aniruddha.
24510 ▼a Scalable Bayesian Network Learning and Its Applications.
260 ▼a [S.l.] : ▼b Carnegie Mellon University., ▼c 2018
260 1 ▼a Ann Arbor : ▼b ProQuest Dissertations & Theses, ▼c 2018
300 ▼a 116 p.
500 ▼a Source: Dissertation Abstracts International, Volume: 80-01(E), Section: B.
500 ▼a Adviser: Ole J. Mengshoel.
5021 ▼a Thesis (Ph.D.)--Carnegie Mellon University, 2018.
520 ▼a 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
520 ▼a 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
520 ▼a 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-
520 ▼a 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
590 ▼a School code: 0041.
650 4 ▼a Computer engineering.
690 ▼a 0464
71020 ▼a Carnegie Mellon University. ▼b Electrical and Computer Engineering.
7730 ▼t Dissertation Abstracts International ▼g 80-01B(E).
773 ▼t Dissertation Abstract International
790 ▼a 0041
791 ▼a Ph.D.
792 ▼a 2018
793 ▼a English
85640 ▼u http://www.riss.kr/pdu/ddodLink.do?id=T14996751 ▼n KERIS
980 ▼a 201812 ▼f 2019
990 ▼a 관리자 ▼b 관리자