A hidden Markov model (HMM) allows us to talk about both observed events (like words that we see in the input) and hidden events (like part-of-speech tags) that we think of as causal factors in our probabilistic model. An HMM is specified by the following components
A first-order hidden Markov model instantiates two simplifying assumptions
First, as with a first-order Markov chain, the probability of a particular state depends only on the previous state:
Markov Assumption: P(qi |q1…qi−1) = P(qi |qi−1)