The HMM is based on augmenting the Markov chain. A Markov chain is a model Markov chain that tells us something about the probabilities of sequences of random variables, states, each of which can take on values from some set. These sets can be words, or tags, or symbols representing anything, for example the weather. A Markov chain makes a very strong assumption that if we want to predict the future in the sequence, all that matters is the current state. All the states before the current state have no impact on the future except via the current state. It's as if to predict tomorrow's weather you could examine today's weather but you weren't allowed to look at yesterday's weather. More formally, consider a sequence of state variables q 1 , q 2 , ..., q i . A Markov model embodies the Markov assumption on the probabilities of this sequence: that