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A Markov chain is a mathematical process that transitions from one state to another within a finite number of possible states. It is a collection of different states and probabilities of a variable, where its future condition or state is substantially dependent on its immediate previous state.
A Markov chain is also known as a discrete time Markov chain (DTMC) or Markov process.
Markov chains are primarily used to predict the future state of a variable or any object based on its past state. It applies probabilistic approaches in predicting the next state. Markov chains are exhibited using directed graphs, which define the current and past state and the probability of transitioning from one state to another.
Markov chains have several implementations in computing and Internet technologies. For example, the PageRank(r) formula employed by Google search uses a Markov chain to calculate the PageRank of a particular Web page. It is also used to predict user behavior on a website based on users' previous preferences or interactions with it.