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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Bibliographic Details
Main Authors: Md. Zulfiquar Ali Bhotto, Tay, Wee Peng
Other Authors: School of Electrical and Electronic Engineering
Format: Article
Language:English
Published: 2019
Subjects:
Online Access:https://hdl.handle.net/10356/102641
http://hdl.handle.net/10220/48151
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Institution: Nanyang Technological University
Language: English