  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.
