Non-Bayesian social learning with observation reuse and soft switching

We propose a non-Bayesian social learning update rule for agents in a network, which minimizes the sum of the Kullback-Leibler divergence between the true distribution generating the agents’ local observations and the agents’ beliefs (parameterized by a hypothesis set), and a weighted varentropy-rel...

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書目詳細資料
Main Authors: Md. Zulfiquar Ali Bhotto, Tay, Wee Peng
其他作者: School of Electrical and Electronic Engineering
格式: Article
語言:English
出版: 2019
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在線閱讀:https://hdl.handle.net/10356/102641
http://hdl.handle.net/10220/48151
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