Department of Mechanical Engineering
2014 Special Seminar
Iris Tien
Systems Engineering
University of California, Berkeley
Bayesian Network Methods for Modeling and Reliability
Assessment of Complex Systems
Feb. 19, 2014 | 1:30 PM | DeWalt Seminar Room, 2164 Martin Hall
abstract:
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.
bio:
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.
www.enme.umd.edu/seminars
For more information: Kim Frye (kfrye@umd.edu)