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Babagholami Mohamadabad, Behnam & Yoon, Sejong & Pavlovic, Vladimir (2016). Decentralized Approximate Bayesian Inference for Distributed Sensor Network. Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence and the Twenty-Eighth Innovative Applications of Artificial Intelligence Conference Retrieved from https://doi.org/doi:10.7282/T3CN75TJ

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Decentralized Approximate Bayesian Inference for Distributed Sensor Network (2016)
Behnam Babagholami Mohamadabad, Sejong Yoon, Vladimir Pavlovic
Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence and the Twenty-Eighth Innovative Applications of Artificial Intelligence Conference.
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VersionAccepted Manuscript (AM)
AbstractBayesian models provide a framework for probabilistic modelling of complex datasets. Many such models are computationally demanding, especially in the presence of large datasets. In sensor network applications, statistical (Bayesian) parameter estimation usually relies on decentralized algorithms, in which both data and computation are distributed across the nodes of the network. In this paper we propose a framework for decentralized Bayesian learning using Bregman Alternating Direction Method of Multipliers (B-ADMM).We demonstrate the utility of our framework, with Mean Field Variational Bayes (MFVB) as the primitive for distributed affine structure from motion (SfM).
Persistent URLhttps://doi.org/doi:10.7282/T3CN75TJ
SubjectsArtificial intelligence, Bayesian statistical decision theory, Sensor networks, Bregman Alternating Direction Method of Multipliers
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