Hypothesis assignment and partial likelihood averaging for cooperative estimation

Paritosh, Parth; Atanasov, Nikolay; Martínez, Sònia

doi:10.1109/cdc40024.2019.9029768

Cited by 4 publications

(1 citation statement)

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“…In contrast, we consider a probabilistic choice of the trending hypothesis. In [18], distributing partial hypotheses sets for agents to track is examined, but we do not consider hypotheses assignment problems in this work. Aiming to improve communication efficiency in detection networks, the work [19] proposes communicating with one randomly sampled agent instead of all neighbors at each time instant.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Social Opinion Formation Under Communication Trends

Kayaalp¹,

Bordignon²,

Sayed³

2022

Preprint

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This work studies the learning process over social networks under partial and random information sharing. In traditional social learning, agents exchange full information with each other while trying to infer the true state of nature. We study the case where agents share information about only one hypothesis, i.e., the trending topic, which can be randomly changing at every iteration. We show that agents can learn the true hypothesis even if they do not discuss it, at rates comparable to traditional social learning. We also show that using one's own belief as a prior for estimating the neighbors' non-transmitted components might create opinion clusters that prevent learning with full confidence. This practice however avoids the complete rejection of the truth.

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Section: Introduction and Related Workmentioning

confidence: 99%