In a (discrete) hidden Markov model, the state is not directly visible, but
output, dependent on the state, is visible. Each state has a probability
distribution over the possible output tokens (could be continuous). Therefore
the sequence of tokens generated by an HMM gives some information about the
sequence of states. Note that the adjective 'hidden' refers to the state
sequence through which the model passes, not to the parameters of the model;
even if the model parameters are known exactly, the model is still 'hidden'.
In other words, a hidden Markov model is a Markov chain of (hidden) states
and for each state a conditional random number generator (distribution).