Stochastic Gradient-Based Distributed Bayesian Estimation in Cooperative Sensor Networks

Jose Cadena, Priyadip Ray, Hao Chen, Braden Soper, Deepak Rajan, Anton Yen, Ryan Goldhahn

Research output: Contribution to journalArticlepeer-review

Abstract

Distributed Bayesian inference provides a full quantification of uncertainty offering numerous advantages over point estimates that autonomous sensor networks are able to exploit. However, fully-decentralized Bayesian inference often requires large communication overheads and low network latency, resources that are not typically available in practical applications. In this paper, we propose a decentralized Bayesian inference approach based on stochastic gradient Langevin dynamics, which produces full posterior distributions at each of the nodes with significantly lower communication overhead. We provide analytical results on convergence of the proposed distributed algorithm to the centralized posterior, under typical network constraints. We also provide extensive simulation results to demonstrate the validity of the proposed approach.

Original languageAmerican English
JournalElectrical and Computer Engineering Faculty Publications and Presentations
StatePublished - 11 Feb 2021

Keywords

  • Bayes methods
  • Monte Carlo methods
  • autonomous systems
  • distributed algorithms
  • network theory(graphs)
  • statistical learning

EGS Disciplines

  • Electrical and Computer Engineering

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