Change point analysis with adaptive mixture models

Adaptive mixture models, an extension of finite mixture models in which the number of terms in the probability density estimate is data-driven, are considered for their utility in investigating change point phenomena in high data rate, nonparametric settings. While probability density estimation is only one small part of exploratory data analysis, the ability of adaptive mixture models to develop these estimates nonparametrically can improve the understanding and detection of change points