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Mathematics

Using Markov Chains to Model Random Processes

Quick fact

Markov chains are named after Andrey Markov, who first studied them in 1906 to prove that the law of large numbers can apply to dependent events, not just independent ones. Today they power Google's PageRank algorithm, which sorts search results by modeling web surfers as a Markov chain.

Why this is interesting

Imagine a game where you move a token on a board based on a coin flip—where you go depends only on where you are now, not on how you got there. How can we predict where the token will be after many moves?