This chapter presents a set of fundamental concepts related to statistical signal processing, at least to the extent necessary to understand the concepts and techniques used in the chapters preceding this one. More specifically, stochastic and random processes (continuous, discrete, causal, stationary, cyclostationary, and ergodic) are presented, together with an analysis of random signals in the frequency domain. The concept of power spectral density and the Wiener-Khinchin theorem are presented, and an equation for the output of a linear time-invariant (LTI) system is estimated when a stochastic signal is applied to its input (this description is also given for the case of a discrete LTI system that receives as input a random sequence). The cross-correlation and power spectral density are calculated, together with innovation and whitening filters. White noise is described in detail, and white noise processes are defined. The chapter concludes with a detailed statistical analysis of quantization error and a presentation of floating-point quantizers.