We consider the problem of identifying a pattern of faults from a set of noisy linear measurements. Unfortunately,
maximum a posteriori probability estimation of the fault pattern is computationally intractable. To solve the fault
identification problem, we propose a non-parametric belief propagation approach. We show empirically that our
belief propagation solver is more accurate than recent state-of-the-art algorithms including interior point methods
and semidefinite programming. Our superior performance is explained by the fact that we take into account both the
binary nature of the individual faults and the sparsity of the fault pattern arising from their rarity.
TalksJoin us for Dror Baron's ITA 2011 talk. Sunday 2/06 - Friday 2/11, UCSD.