Logo
Munich Personal RePEc Archive

A probabilistic interpretation of the constant gain algorithm

Berardi, Michele (2019): A probabilistic interpretation of the constant gain algorithm.

[img] PDF
MPRA_paper_94023.pdf

Download (135kB)

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.

Atom RSS 1.0 RSS 2.0

Contact us: mpra@ub.uni-muenchen.de

This repository has been built using EPrints software.

MPRA is a RePEc service hosted by Logo of the University Library LMU Munich.