TY - GEN
T1 - Marginal likelihoods for distributed estimation of graphical model parameters
AU - Meng, Zhaoshi
AU - Wei, Dennis
AU - Hero, Alfred O.
AU - Wiesel, Ami
PY - 2013
Y1 - 2013
N2 - This paper considers the estimation of graphical model parameters with distributed data collection and computation. We first discuss the use and limitations of well-known distributed methods for marginal inference in the context of parameter estimation. We then describe an alternative framework for distributed parameter estimation based on maximizing marginal likelihoods. Each node independently estimates local parameters through solving a low-dimensional convex optimization with data collected from its local neighborhood. The local estimates are then combined into a global estimate without iterative message-passing. We provide an asymptotic analysis of the proposed estimator, deriving in particular its rate of convergence. Numerical experiments validate the rate of convergence and demonstrate performance equivalent to the centralized maximum likelihood estimator.
AB - This paper considers the estimation of graphical model parameters with distributed data collection and computation. We first discuss the use and limitations of well-known distributed methods for marginal inference in the context of parameter estimation. We then describe an alternative framework for distributed parameter estimation based on maximizing marginal likelihoods. Each node independently estimates local parameters through solving a low-dimensional convex optimization with data collected from its local neighborhood. The local estimates are then combined into a global estimate without iterative message-passing. We provide an asymptotic analysis of the proposed estimator, deriving in particular its rate of convergence. Numerical experiments validate the rate of convergence and demonstrate performance equivalent to the centralized maximum likelihood estimator.
UR - http://www.scopus.com/inward/record.url?scp=84894147256&partnerID=8YFLogxK
U2 - 10.1109/CAMSAP.2013.6714010
DO - 10.1109/CAMSAP.2013.6714010
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AN - SCOPUS:84894147256
SN - 9781467331463
T3 - 2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013
SP - 73
EP - 76
BT - 2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013
T2 - 2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013
Y2 - 15 December 2013 through 18 December 2013
ER -