Markov chains, named after Andrey Markov, are mathematical systems that hop from one “state” (a situation or set of values) to another. For example, if you made a Markov chain model of a baby’s behavior, you might include “playing,” “eating”, “sleeping,” and “crying” as states, which together with other behaviors could form a ‘state space’: a list of all possible states. In addition, on top of the state space, a Markov chain tells you the probability of hopping, or “transitioning,” from one state to any other state — -e.g., the chance that a baby currently playing will fall asleep in the next five minutes without crying first. Read more about how Markov Chain works in this interactive article by Victor Powell.
Click through for a fun example of headline generation.