Distributed Bayesian Estimation of Linear Models with Unknown Observation Covariances

Yunlong Wang, Petar M. Djurić

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

In this paper, we address the problem of distributed Bayesian estimation in networks of agents over a given undirected graph. The agents observe data represented by a general linear model with unknown covariance matrices. The agents try to reach consensus on the belief on the unknown linear parameters based on their private signals and information provided by their neighbors. The belief is defined by the posterior distribution of the parameters. After deriving the Bayesian belief held by a fictitious fusion center, we present a consensus-based solution where the agents reach the belief of the fusion center. According to our scheme, at every time instant, each agent carries out three operations: a) receives private noisy measurements; b) exchanges information about its belief with its neighbors; and c) updates its belief with the new information. We show that with the proposed method, the Kullback-Leibler divergence between the beliefs of the agents and the fusion center converges to zero. We demonstrate the performance of the method by computer simulations.

Original languageEnglish (US)
Article number7294691
Pages (from-to)1962-1971
Number of pages10
JournalIEEE Transactions on Signal Processing
Volume64
Issue number8
DOIs
StatePublished - Apr 15 2016

Bibliographical note

Publisher Copyright:
© 2015 IEEE.

Keywords

  • Bayesian inference
  • average consensus
  • covariance estimation
  • distributed estimation
  • linear model

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