The EM algorithm for graphical association models with missing data
Computational Statistics & Data Analysis - Special issue dedicated to Toma´sˇ Havra´nek
Fault-local distributed mending (extended abstract)
Proceedings of the fourteenth annual ACM symposium on Principles of distributed computing
Distributed cooperative Bayesian learning strategies
COLT '97 Proceedings of the tenth annual conference on Computational learning theory
Estimating dependency structure as a hidden variable
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Stabilizing time-adaptive protocols
Theoretical Computer Science
Introduction to Bayesian Networks
Introduction to Bayesian Networks
Collective Mining of Bayesian Networks from Distributed Heterogeneous Data
Knowledge and Information Systems
Asynchronous and fully self-stabilizing time-adaptive majority consensus
OPODIS'05 Proceedings of the 9th international conference on Principles of Distributed Systems
Association rule mining in peer-to-peer systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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In this paper we present a majority-based method to learn Bayesian network structure from databases distributed over a peer-to-peer network. The method consists of a majority learning algorithm and a majority consensus protocol. The majority learning algorithm discovers the local Bayesian network structure based on the local database and updates the structure once new edges are learnt from neighboring nodes. The majority consensus protocol is responsible for the exchange of the local Bayesian networks between neighboring nodes. The protocol and algorithm are executed in tandem on each node. They perform their operations asynchronously and exhibit local communications. Simulation results verify that all new edges, except for edges with confidence levels close to the confidence threshold, can be discovered by exchange of messages with a small number of neighboring nodes.