Markov chains are systems which transition from one state to another based only upon their current state. They are used widely in various statistical domains to generate sequences based upon probabilities.
Markov chains (named after their creator, Andrey Markov) are systems which transition from one state to another based only upon their current state. They are memoryless processes which are semi-random, i.e. where each state change having an associated probability.
Due to their statical nature, Markov chains are suitable for simulating complex real-life processes where probabilities are well known. They are used in a wide variety of fields, with uses too in-depth to list here; an exhaustive list can be found on the associated Wikipedia page.
In programming, they are especially popular for manipulating human languages - Markov text generators are especially popular applications of Markov chains.
