A theory of diagnosis from first principles
Artificial Intelligence
Artificial Intelligence
Automatica (Journal of IFAC)
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SIGCOMM '93 Conference proceedings on Communications architectures, protocols and applications
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Cluster Computing
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IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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Automatica (Journal of IFAC)
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IEEE Transactions on Information Theory
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IEEE Transactions on Information Theory
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IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
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IEEE Transactions on Information Theory
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Proceedings of the 18th ACM conference on Information and knowledge management
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Signal Processing
Estimation of faults in DC electrical power system
ACC'09 Proceedings of the 2009 conference on American Control Conference
Probabilistic management of OCR data using an RDBMS
Proceedings of the VLDB Endowment
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We consider the problem of estimating a pattern of faults, represented as a binary vector, from a set of measurements. The measurements can be noise corrupted real values, or quantized versions of noise corrupted signals, including even 1-bit (sign) measurements. Maximum a posteriori probability (MAP) estimation of the fault pattern leads to a difficult combinatorial optimization problem, so we propose a variation in which an approximate maximum a posteriori probability estimate is found instead, by solving a convex relaxation of the original problem, followed by rounding and simple local optimization. Our method is extremely efficient, and scales to very large problems, involving thousands (or more) of possible faults and measurements. Using synthetic examples, we show that the method performs extremely well, both in identifying the true fault pattern, and in identifying an ambiguity group, i.e., a set of alternate fault patterns that explain the observed measurements almost as well as our estimate.