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. In a Markov process, each state has a set of possible next states, along with probabilities that dictate the likelihood of moving to each next state. These probabilities can be represented in a transition matrix, which helps in predicting future states based on the current state.