Sequential Methods for Detecting a Change in the Distribution of an Episodic Process

Abstract

A new class of stochastic processes called episodic processes is introduced to model the statistical regularity of data observed in several applications in cyberphysical systems, neuroscience, and medicine. Algorithms are proposed to detect a change in the distribution of episodic processes. The algorithms can be computed recursively using finite memory and are shown to be asymptotically optimal for well-defined Bayesian or minimax stochastic optimization formulations. The application of the developed algorithms to detect a change in waveform patterns is also discussed.