A Markov Process is a mathematical model that describes a system which transitions from one state to another, where the next state depends only on the current state and not on the previous states. This property is known as the Markov property. Markov processes are widely used in various fields, including statistics, economics, and computer science, to model random systems. There are different types of Markov processes, such as Markov Chains and Markov Decision Processes. In a Markov Chain, the system moves between a finite number of states, while a Markov Decision Process incorporates decision-making, allowing for actions that influence future states. These models help in predicting outcomes and optimizing decisions in uncertain environments.