This paper considers the problem of distributed adaptive linear parameter estimation in multiagent inference networks. Local sensing model information is only partially available at the agents, and interagent communication is assumed to be unpredictable. The paper develops a generic mixed time-scale stochastic procedure consisting of simultaneous distributed learning and estimation, in which the agents adaptively assess their relative observation quality over time and fuse the innovations accordingly. Under rather weak assumptions on the statistical model and the interagent communication, it is shown that, by properly tuning the consensus potential with respect to the innovation potential, the asymptotic information rate loss incurred in the learning process may be made negligible. As such, it is shown that the agent estimates are asymptotically efficient, in that their asymptotic covariance coincides with that of a centralized estimator (the inverse of the centralized Fisher information rate for Gaussian systems) with perfect global model information and having access to all observations at all times. The proof techniques are mainly based on convergence arguments for non-Markovian mixed time-scale stochastic approximation procedures. Several approximation results developed in the process are of independent interest.


  1. multiagent systems
  2. distributed estimation
  3. mixed time-scale
  4. stochastic approximation
  5. asymptotically efficient
  6. adaptive algorithms

MSC codes

  1. 93E10
  2. 93E35
  3. 60G35
  4. 94A13
  5. 62M09
  6. 62M05

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Information & Authors


Published In

cover image SIAM Journal on Control and Optimization
SIAM Journal on Control and Optimization
Pages: 2200 - 2229
ISSN (online): 1095-7138


Submitted: 19 September 2011
Accepted: 22 January 2013
Published online: 28 May 2013


  1. multiagent systems
  2. distributed estimation
  3. mixed time-scale
  4. stochastic approximation
  5. asymptotically efficient
  6. adaptive algorithms

MSC codes

  1. 93E10
  2. 93E35
  3. 60G35
  4. 94A13
  5. 62M09
  6. 62M05



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