Random Variable & Random Process: Problem Solving Techniques – Free Udemy Courses

In this course, you will learn step-by-step procedures to solve questions on Random variables and Random processes. I will discuss the problems on probability density function, cumulative probability distribution function, conditional probability, joint probability, Bayes rule, Statistical averages such as mean, mean square value, power spectral density, autocorrelation, cross-correlation, Uniform distribution, Gaussian random variable, Transmission of a Random process through a linear filter, properties of the autocorrelation function, properties of power spectral density.

Random signals are encountered in every practical communication system. Some examples of Random signals are voice signals, television signals, digital computer data, and electrical noise. A signal is random if it is not possible to predict the exact value of the signal in advance whereas a signal is deterministic if it is possible to predict the exact value of the signal in advance. The mathematical discipline that deals with the statistical characterization of random signals are probability theory.