A Markov model is a type of stochastic model that models the probability of transitioning between different states over time. It is named after Russian mathematician Andrey Markov and is commonly used in fields such as mathematics, statistics, economics, and biological sciences. Markov models are characterized by the Markov property, which states that the future state of the system only depends on the present state and not on the sequence of events that preceded it. This makes Markov models useful for modeling systems with random and unpredictable behavior, such as weather patterns, financial markets, and biological processes. They are also used in machine learning and artificial intelligence for tasks such as speech recognition and natural language processing.