Journal
IEEE TRANSACTIONS ON SIGNAL PROCESSING
Volume 61, Issue 9, Pages 2256-2266Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSP.2013.2251336
Keywords
Mean objective cost of uncertainty (MOCU); objective-based uncertainty quantification (UQ); robust network intervention; robust operator design
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Funding
- National Science Foundation through NSF [CCF-1149544]
- National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health [R21DK092845]
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Real-world problems often involve complex systems that cannot be perfectly modeled or identified, and many engineering applications aim to design operators that can perform reliably in the presence of such uncertainty. In this paper, we propose a novel Bayesian framework for objective-based uncertainty quantification (UQ), which quantifies the uncertainty in a given system based on the expected increase of the operational cost that it induces. This measure of uncertainty, called MOCU (mean objective cost of uncertainty), provides a practical way of quantifying the effect of various types of system uncertainties on the operation of interest. Furthermore, the proposed UQ framework provides a general mathematical basis for designing robust operators, and it can be applied to diverse applications, including robust filtering, classification, and control. We demonstrate the utility and effectiveness of the proposed framework by applying it to the problem of robust structural intervention of gene regulatory networks, an important application in translational genomics.
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