A probabilistic interpretation of the constant gain algorithm
Michele Berardi · Munich Personal RePEc Archive (Ludwig Maximilian University of Munich) · 2019
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.