  Online detection of changes in stochastic systems, referred to as sequential
change detection or quickest change detection, is an important research topic
in statistics, signal processing, and information theory, and has a wide range
of applications. This survey starts with the basics of sequential change
detection, and then moves on to generalizations and extensions of sequential
change detection theory and methods. We also discuss some new dimensions that
emerge at the intersection of sequential change detection with other areas,
along with a selection of modern applications and remarks on open questions.
