Recovering latent time-series from their observed sums: network tomography with particle filters

@inproceedings{Airoldi2004RecoveringLT,
  title={Recovering latent time-series from their observed sums: network tomography with particle filters},
  author={Edoardo M. Airoldi and Christos Faloutsos},
  booktitle={KDD},
  year={2004}
}
Hidden variables, evolving over time, appear in multiple settings, where it is valuable to recover them, typically from observed sums. Our driving application is 'network tomography', where we need to estimate the origin-destination (OD) traffic flows to determine, e.g., who is communicating with whom in a local area network. This information allows network engineers and managers to solve problems in design, routing, configuration debugging, monitoring and pricing. Unfortunately the direct… CONTINUE READING
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