Markov Chain

Markov chains are used to model probabilities using information that can be encoded in the current state. Each state has a certain probability of transitioning to each other state, so each time you are in a state and want to transition, a markov chain can predict outcomes based on pre-existing probability data. More technically, information is put into a matrix and a vector - also called a column matrix - and with many iterations, a collection of probability vectors makes up Markov chains. To determine the transition probabilities, you have to "train" your Markov Chain on some input corpus.