Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Distributed cooperative Bayesian learning strategies
COLT '97 Proceedings of the tenth annual conference on Computational learning theory
The small-world phenomenon: an algorithmic perspective
STOC '00 Proceedings of the thirty-second annual ACM symposium on Theory of computing
P-Grid: A Self-Organizing Access Structure for P2P Information Systems
CooplS '01 Proceedings of the 9th International Conference on Cooperative Information Systems
Nonparametric belief propagation for self-calibration in sensor networks
Proceedings of the 3rd international symposium on Information processing in sensor networks
Collective Mining of Bayesian Networks from Distributed Heterogeneous Data
Knowledge and Information Systems
Vivaldi: a decentralized network coordinate system
Proceedings of the 2004 conference on Applications, technologies, architectures, and protocols for computer communications
Correctness of Local Probability Propagation in Graphical Models with Loops
Neural Computation
Towards interactive smart spaces
Journal of Ambient Intelligence and Smart Environments - Context Awareness
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In this paper, we will present an efficient approach for distributed inference We use belief propagation's message-passing algorithm on top of a DHT storing a Bayesian network Nodes in the DHT run a variant of the spring relaxation algorithm to redistribute the Bayesian network among them Thereafter correlated data is stored close to each other reducing the message cost for inference We simulated our approach in Matlab and show the message reduction and the achieved load balance for random, tree-shaped, and scale-free Bayesian networks of different sizes. As possible application, we envision a distributed software knowledge base maintaining encountered software bugs under users' system configurations together with possible solutions for other users having similar problems Users would not only be able to repair their system but also to foresee possible problems if they would install software updates or new applications.