  Change-point analysis is a flexible and computationally tractable tool for
the analysis of times series data from systems that transition between discrete
states and whose observables are corrupted by noise. The change-point algorithm
is used to identify the time indices (change points) at which the system
transitions between these discrete states. We present a unified
information-based approach to testing for the existence of change points. This
new approach reconciles two previously disparate approaches to Change-Point
Analysis (frequentist and information-based) for testing transitions between
states. The resulting method is statistically principled, parameter and prior
free and widely applicable to a wide range of change-point problems.
