Approximate Decentralized Bayesian Inference
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How_Approximate decentralized.pdf
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548.99 KB
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Adobe PDF
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0587bae8f199502ba71c53061a924ba9
Author(s)
Campbell, Trevor David
How, Jonathan P.
Date Issued
July 2014
Journal
Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, UAI 2014
Publisher
Association for Uncertainty in Artificial Intelligence Press
Citation
Campbell, Trevor and Jonathan How. "Approximate Decentralized Bayesian Inference." 30th Conference on Uncertainty in Artificial Intelligence, UAI 2014, Quebec City, Quebec, Canada, July 23-27, 2014. p.1-10.
Version
Author's final manuscript
Abstract
This paper presents an approximate method for performing Bayesian inference in models with conditional independence over a decentralized network of learning agents. The method first employs variational inference on each individual learning agent to generate a local approximate posterior, the agents transmit their local posteriors to other agents in the network, and finally each agent combines its set of received local posteriors. The key insight in this work is that, for many Bayesian models, approximate inference schemes destroy symmetry and dependencies in the model that are crucial to the correct application of Bayes’ rule when combining the local posteriors. The proposed method addresses this issue by including an additional optimization step in the combination procedure that accounts for these broken dependencies. Experiments on synthetic and real data demonstrate that the decentralized method provides advantages in computational performance and predictive test likelihood over previous batch and distributed methods.
Description
URL to accepted papers on conference site
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
http://auai.org/uai2014/acceptedPapers.shtml
