Fault Identification Via Nonparametric Belief Propagation

By Danny Bickson, Dror Baron, A. Ihler, Harel Avissar, Danny Dolev. 2009

21 citations recorded by the source corpus.

We consider the problem of identifying a pattern of faults from a set of noisy linear measurements. Unfortunately, maximum a posteriori (MAP) probability estimation of the fault pattern is computationally intractable. To solve the fault identification problem, we propose a nonparametric belief propagation (NBP) 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.

Subfields: Computational Mechanics, Computer Networks and Communications, Control and Systems Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Mechanics

1. Carlos Guestrin
2. Yucheng Low
3. Arthur Gretton
4. Danny Bickson
5. Le Song
6. Peter Richtárik
7. Martin Takác
8. Matilde Gargiani

In Computer Networks and Communications

1. Le Song
2. Carlos Guestrin
3. Yucheng Low
4. Arthur Gretton
5. Danny Bickson
6. Andreas Krause
7. Gregory F. Coppola

In Control and Systems Engineering

1. Arthur Gretton
2. Carlos Guestrin
3. Le Song

Citations in the corpus

• Kernel Belief Propagation (2011, methodology): “An overview of approximation approaches can be found in Bickson et al. (2011); we used an efficient KD-tree method of Ihler et al. (2003) for performing the approximation step.”