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In this paper, the authors 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.
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