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Distributed Bayesian Inference Over Sensor Networks.

Baijia Ye, Jiahu Qin, Weiming Fu

    IEEE Transactions on Cybernetics
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    Two new distributed variational Bayesian (VB) algorithms enhance sensor network analysis. These methods improve estimation and clustering for synchronous and asynchronous networks, offering robust and efficient distributed inference.

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    Area of Science:

    • Machine Learning
    • Distributed Systems
    • Statistical Inference

    Background:

    • Distributed variational Bayesian (VB) inference is crucial for analyzing large-scale sensor network data.
    • Existing methods often struggle with synchronous or asynchronous network constraints.

    Purpose of the Study:

    • To propose novel distributed VB algorithms for conjugate-exponential models in synchronous and asynchronous sensor networks.
    • To enhance the performance of distributed inference in terms of accuracy, robustness, and speed.

    Main Methods:

    • Developed a penalty-based distributed VB (PB-DVB) algorithm using Kullback-Leibler (KL) divergence for synchronous networks.
    • Designed a token-passing-based distributed VB (TPB-DVB) algorithm incorporating stochastic variational inference for asynchronous networks.

    Main Results:

    • The PB-DVB algorithm demonstrated strong estimation/inference capabilities, robustness to initialization, and fast convergence.
    • The TPB-DVB algorithm outperformed existing token-passing methods in distributed clustering tasks.

    Conclusions:

    • The proposed PB-DVB and TPB-DVB algorithms offer effective solutions for distributed Bayesian inference in sensor networks.
    • These algorithms provide significant improvements for applications like Gaussian Mixture Models (GMM) in various network settings.