ABSTRACT
One of the central aims of modeling is prediction. To this effect, models are trained to achieve as good predictions as possible by choosing the decision variables (model parameters) so that a chosen norm of prediction errors (typically the squared 2-norm) is minimized. Therefore, it is important to formally study the concept of prediction and learn the mathematics of developing predictor expressions for a given model structure. This chapter is devoted to this purpose.
