A Markov process is a mathematical model that describes a system that 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 decisions that can influence the transitions. These models help in predicting future states based on current information.