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May 28, 2014

Mtg: Bayesian Methods for System Health Management

by @ 4:51 pm. Filed under ALL, BioEngineering, Computers/Software, Engineering Mgmt

THURSDAY June 19, 2014
SCV Robotics and Automation Chapter
– diagnostics, inference, machine learning, junction trees …
Speaker: Prof. Ole J. Mengshoel, ECE, Carnegie Mellon University Silicon Valley
Time: Presentation at 7:00 PM
Cost: None
Place: Carnegie Mellon University Silicon Valley, Room 118, Building 23 — S. Akron Road, Moffett Field
RSVP: not required

Research on Bayesian methods, including Bayesian networks, has enjoyed significant progress in recent years. In addition, Bayesian networks have been successfully employed in many applications, including model-based diagnostics, natural language understanding, probabilistic risk analysis, intelligence analysis, and error correction coding. In addition to being well-suited to automated analysis, these probabilistic graphical models are also very useful in structuring visualizations that are helpful to users and developers.
In this talk, I will discuss a few areas where progress in Bayesian network modeling and computation has recently been made, with a focus on System Health Management. I will discuss modeling and computational techniques leading to strong experimental results in real-world systems, for example the Advanced Diagnostic and Prognostic Testbed (ADAPT). ADAPT is an electrical power system located at the NASA Ames Research Center. I will discuss algorithms for exact and approximate inference and machine learning, including how Bayesian network computation can be sped up. If time permits, I will also discuss results for the use of graphics processing units (GPUs) to perform belief propagation in junction trees.


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