An Introduction to Markov Chains - KDnuggets

Markov chains are a type of mathematical system that undergoes transitions from one state to another according to certain probabilistic rules. They were first introduced by Andrey Markov in 1906 as a way to model the behavior of random processes, and have since been applied to a wide range of fields, including physics, biology, economics, statistics, machine learning, and computer science. Markov chains are named after Andrey Markov, a Russian mathematician who is credited with developing the theory of these systems in the early 20th century. Markov was interested in understanding the behavior of random processes, and he developed the theory of Markov chains as a way to model such processes. Markov chains are often used to model systems that exhibit memoryless behavior, where the system's future behavior is not influenced by its past behavior.