Stochastic Learning of Time-Varying Parameters in Random Environment

The problem of learning in nonstationary environment is formulated as that of estimating time-varying parameters of a probability distribution which characterizes the process under study. Dynamic stochastic approximation algorithms are proposed to estimate the unknown time-varying parameters in a recursive fashion. Both supervised and nonsupervised learning schemes are discussed and their convergence properties are investigated. An accelerated scheme for the possible improvement of the dynamic algorithm is given. Numerical examples and an application of the proposed algorithm to a problem in weather forecasting are presented.