Random processes are mathematical models that encapsulate the randomness observed in the evolution of systems over time. They serve as essential tools for analyzing and predicting the occurrence of random events within these systems. A random process can be viewed as the temporal evolution of a set of random variables or, in other words, as a collection of random events occurring sequentially. Typically, random processes consist of two fundamental components: the state space, which encompasses all possible states of the process, and the transition probability, which quantifies the likelihood of moving from one state to another. Depending on the context of application, stochastic processes can be categorized into discrete-time and continuous-time stochastic processes. Discrete-time processes involve observations at regular time intervals, each associated with a specific state, with the Markov chain being a prime example. In contrast, continuous-time processes allow for observations at any moment, with each instant corresponding to a particular state, exemplified by Brownian motion, where the state space is the set of real numbers and transition probabilities follow a Gaussian distribution.