Continuously evolving classification of signals corrupted by an abrupt change
Bayes decision theory is based on the assumption that the decision problem is posed in probabilistic terms, and that all of the relevant probability values are known. The aim of this paper is to show how blind sliding window AR modeling is corrupted by an abrupt model change and to derive a statistical study of these parameters.