  Gaussian processes (GPs) are versatile tools that have been successfully
employed to solve nonlinear estimation problems in machine learning, but that
are rarely used in signal processing. In this tutorial, we present GPs for
regression as a natural nonlinear extension to optimal Wiener filtering. After
establishing their basic formulation, we discuss several important aspects and
extensions, including recursive and adaptive algorithms for dealing with
non-stationarity, low-complexity solutions, non-Gaussian noise models and
classification scenarios. Furthermore, we provide a selection of relevant
applications to wireless digital communications.
