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This paper examines the joint problem of detection and identification of asudden and unobservable change in the probability distribution function (pdf)of a sequence of independent and identically distributed (i.i.d.) randomvariables to one of finitely many alternative pdf's. The objective is quickdetection of the change and accurate inference of the ensuing pdf. Following aBayesian approach, a new sequential decision strategy for this problem isrevealed and is proven optimal. Geometrical properties of this strategy aredemonstrated via numerical examples.

Joint Detection and Identification of an Unobservable Change in the Distribution of a Random Sequence