Machine Learning, Markov Chains, Statistics, NLP, Fun.
obtain the conditional probabilities for the next state’s values by taking the current state, and looking at its corresponding row. After that, if we repeatedly sample the discrete distribution described by the n-th state’s row, we may model a succession of states of arbitrary length. Markov Chains