A probabilistic interpretation of the constant gain algorithm
Michele Berardi ()
MPRA Paper from University Library of Munich, Germany
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)
New Economics Papers: this item is included in nep-cmp and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2) Track citations by RSS feed
Downloads: (external link)
https://mpra.ub.uni-muenchen.de/94023/1/MPRA_paper_94023.pdf original version (application/pdf)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:94023
Access Statistics for this paper
More papers in MPRA Paper from University Library of Munich, Germany Ludwigstraße 33, D-80539 Munich, Germany. Contact information at EDIRC.
Bibliographic data for series maintained by Joachim Winter ().