Stochastic Gradient-Based Distributed Bayesian Estimation in Cooperative Sensor Networks

Journal Article · Thu Feb 11 00:00:00 EST 2021 · IEEE Transactions on Signal Processing

DOI:https://doi.org/10.1109/tsp.2021.3058765· OSTI ID:1811776

Cadena, Jose ^[1]; Ray, Priyadip ^[1]; Chen, Hao ^[2]; Soper, Braden ^[1]; Rajan, Deepak ^[3]; Yen, Anton ^[1]; Goldhahn, Ryan ^[1]

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1. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
2. Boise State Univ., ID (United States)
3. Voleon Group, Berkeley, CA (United States)

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. Finally, we also provide extensive simulation results to demonstrate the validity of the proposed approach.

• Research Organization: Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
• Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA)
• Grant/Contract Number: AC52-07NA27344
• OSTI ID: 1811776
• Report Number(s): LLNL-JRNL--808880; 1015017
• Journal Information: IEEE Transactions on Signal Processing, Journal Name: IEEE Transactions on Signal Processing Vol. 69; ISSN 1053-587X
• Publisher: IEEE
• Country of Publication: United States
• Language: English

Related Subjects

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Monte Carlo methods
autonomous systems
distributed algorithms
electronic and electrical engineering
mathematics and computing
network theory (graphs)
statistical learning