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A probabilistic interpretation of the constant gain algorithm

Michele Berardi ()

MPRA Paper from University Library of Munich, Germany

Abstract: This paper proposes a novel interpretation of the constant gain learning algorithm through a probabilistic setting with Bayesian updating. Such framework allows to understand the gain coefficient in terms of the probability of changes in the estimated quantity.

Keywords: Bayesian learning; adaptive learning; constant gain. (search for similar items in EconPapers)
JEL-codes: C63 D83 D84 D90 (search for similar items in EconPapers)
Date: 2019-05-19
New Economics Papers: this item is included in nep-cmp and nep-ore
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Citations: View citations in EconPapers (2) Track citations by RSS feed

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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:94023

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