Nonasymptotic Concentration Rates in Cooperative Learning-Part I: Variational Non-Bayesian Social Learning

In this article, we studied the problem of cooperative inference where a group of agents interacts over a network and seeks to estimate a joint parameter that best explains a set of network-wide observationsusing local information only. Agents do not know the network topology or the observations of...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	IEEE transactions on control of network systems Ročník 9; číslo 3; s. 1128 - 1140
Hlavní autoři:	Uribe, Cesar A., Olshevsky, Alexander, Nedic, Angelia
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Piscataway IEEE 01.09.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Algorithms Bayes methods Bayesian analysis Computational modeling Cooperative learning Distributed inference estimation over networks Hypotheses Inference Machine learning Mathematical models Maximum likelihood estimation Mirrors Network topologies non-Bayesian social learning nonasymptotic rates Numerical analysis Optimization Parameters Probability distribution functions Random variables Stochastic processes
ISSN:	2325-5870, 2372-2533
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Buďte první, kdo okomentuje tento záznam!