Data-efficient quickest change detection with unknown post-change distribution

Abstract

The problem of quickest change detection is studied, where there is an additional constraint on the cost of observations used before the change point and where the post-change distribution is unknown. An algorithm is proposed for the case where there are finite number of possibilities for the unknown post-change distribution. It is shown that if the post-change family of distributions satisfies some additional conditions, then the proposed algorithm is asymptotically optimal uniformly for all possible post-change distributions.