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ME Special Seminar | Bayesian Network Methods for Modeling & Reliability Assessment of Complex Sys.
Wednesday, February 19, 2014
1:30 p.m.
DeWalt Seminar Room, 2164 Martin Hall
For More Information:
Kimberley Frye

Mechanical Engineering Special Seminar Series

Bayesian Network Methods for Modeling and Reliability Assessment of Complex Systems

Speaker: Iris Tien
Systems Engineering
University of California, Berkeley

The Bayesian network (BN) is an ideal tool for modeling and assessing the reliability of complex systems, particularly when information about the system and its components is uncertain and evolves in time. The major obstacle to the widespread use of BNs for system reliability analysis, however, is the limited size and complexity of the system that can be tractably modeled as a BN. This is due to the exponentially increasing number of elements that must be stored in the conditional probability table (CPT) for the system node in the BN as the number of components in the system increases.

In this seminar, I will present novel compression and inference algorithms that I have developed to address this limitation. The algorithms utilize compression techniques to achieve orders of magnitude savings in memory storage for the system CPT. In addition, heuristics developed to improve the computational efficiency of the algorithms are presented.

Iris Tien will receive her Ph.D. in Systems Engineering from the University of California, Berkeley. She received her M.S. in Civil and Environmental Engineering in 2010, and graduated High Honors with a B.S. in Civil and Environmental Engineering and a Minor in English in 2008 from UC Berkeley. For her research work in developing BN methods for system modeling and reliability analysis, Tien was awarded the Student Paper Award from the ASCE-EMI Probabilistic Methods Committee at the 2013 Engineering Mechanics Institute Conference. Tien is a recipient of the Regents’ and Chancellor’s Scholarship, University of California Chancellor’s Fellowship for Graduate Study, National Science Foundation Graduate Research Fellowship, and National Science Foundation Engineering Innovation Fellowship.

Link to PDF flyer.

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