  Changes in the statistical properties of a stochastic process are typically
assumed to occur via change-points, which demark instantaneous moments of
complete and total change in process behavior. In cases where these transitions
occur gradually, this assumption can result in a reduced ability to properly
identify and respond to process change. With this observation in mind, we
introduce a novel change-dynamic model for the online detection of gradual
change in a Bayesian framework, in which change-points are used within a
hierarchical model to indicate moments of gradual change onset or termination.
We apply this model to synthetic data and EEG readings drawn during epileptic
seizure, where we find our change-dynamic model can enable faster and more
accurate identification of gradual change than traditional change-point models
allow.
