A hidden Markov model (HMM) is a triple (p,A,B).
p=(pi) the vector of the initial state probabilities
A=(aij) the state transition matrix; Pr(xit|xjt-1)
B=(bij) the confusion matrix; Pr(yi|xj)
Each probability in the state transition matrix and in the confusion matrix is time independent - that is, the matrices do not change in time as the system evolves. In practice, this is one of the most unrealistic assumptions of Markov models about real processes.
