  We consider a unified framework of sequential change-point detection and
hypothesis testing modeled by means of hidden Markov chains. One observes a
sequence of random variables whose distributions are functionals of a hidden
Markov chain. The objective is to detect quickly the event that the hidden
Markov chain leaves a certain set of states, and to identify accurately the
class of states into which it is absorbed. We propose computationally tractable
sequential detection and identification strategies and obtain sufficient
conditions for the asymptotic optimality in two Bayesian formulations.
Numerical examples are provided to confirm the asymptotic optimality and to
examine the rate of convergence.
