Entropy Approximation for Active Fault Diagnosis

We address the problem of test selection for fault diagnosis on a Bayesian network, which requires several entropy terms whose exact computation is intractable. We propose an approximate approach that utilizes the loopy belief propagation infrastructure to simultaneously compute approximations of marginal and conditional entropies on multiple subsets of nodes. We apply the method to active probing for fault diagnosis in computer networks, and present promising empirical results on realistic Internet-like topology graphs.

By: Alice X. Zheng; Irina Rish; Alina Beygelzimer

Published in: RC23441 in 2004


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