  We consider the problem of tracking an unknown time varying parameter that
characterizes the probabilistic evolution of a sequence of independent
observations. To this aim, we propose a stochastic gradient descent-based
recursive scheme in which the log-likelihood of the observations acts as time
varying gain function. We prove convergence in mean-square error in a suitable
neighbourhood of the unknown time varying parameter and illustrate the details
of our findings in the case where data are generated from distributions
belonging to the exponential family.
