The chapter discusses the concepts of randomness and random variables, defining a random variable as a numerical assignment to the outcomes of experiments. It differentiates between discrete-value and continuous-value random variables, explaining their properties and distributions. The chapter also covers distribution functions, probability density functions, and moments of random variables, including expectation and variance. It introduces the concept of stochastic processes, which are functions of time that assign numerical values to outcomes of experiments, and discusses their properties such as stationarity and ergodicity. The chapter further explores correlation functions, autocorrelation, and crosscorrelation, and provides examples of these concepts in action. Additionally, it delves into linear prediction and Wiener filters, explaining how to design optimal filters for signal estimation and filtering. The chapter concludes with a detailed example of designing an IIR Wiener filter for optimal signal estimation and smoothing.The chapter discusses the concepts of randomness and random variables, defining a random variable as a numerical assignment to the outcomes of experiments. It differentiates between discrete-value and continuous-value random variables, explaining their properties and distributions. The chapter also covers distribution functions, probability density functions, and moments of random variables, including expectation and variance. It introduces the concept of stochastic processes, which are functions of time that assign numerical values to outcomes of experiments, and discusses their properties such as stationarity and ergodicity. The chapter further explores correlation functions, autocorrelation, and crosscorrelation, and provides examples of these concepts in action. Additionally, it delves into linear prediction and Wiener filters, explaining how to design optimal filters for signal estimation and filtering. The chapter concludes with a detailed example of designing an IIR Wiener filter for optimal signal estimation and smoothing.