  The paper addresses a joint sequential changepoint detection and
identification/isolation problem for a general stochastic model, assuming that
the observed data may be dependent and non-identically distributed, the prior
distribution of the change point is arbitrary, and the post-change hypotheses
are composite. The developed detection-identification theory generalizes the
changepoint detection theory developed by Tartakovsky (2019) to the case of
multiple composite post-change hypotheses when one has not only to detect a
change as quickly as possible but also to identify (or isolate) the true
post-change distribution. We propose a multi-hypothesis change
detection-identification rule and show that it is nearly optimal, minimizing
moments of the delay to detection as the probability of a false alarm and the
probabilities of misidentification go to zero.
