Change point detection algorithms have numerous applications in areas of medical condition monitoring, fault industrial processes, human activity analysis, climate change detection, and speech recognition. We consider the problem on compositional multivariate data (each sample is a probability mass function), which practically important sub-class general data. While change-point well studied un...

Precise identification of the time when a process has changed enables process engineers to search for a potential special cause more effectively. In this paper, we develop change point estimation methods for a Poisson process in a Bayesian framework. We apply Bayesian hierarchical models to formulate the change point where there exists a step < /div> change, a linear trend and a known multip...

This paper proposes a method for buildings change detection. Vertical edges of buildings are extracted in pairs of aerial images respectively, taking advantage of the character that vertical edges intersect at their vanishing point without fail. The ground coordinates of the vertices on each vertical edge are calculated according to projection difference and the principle of frontage intersecti...

We study the multivariate nonparametric change point detection problem, where data are a sequence of independent $p$ -dimensional random vectors whose distributions piecewise-constant with Lipschitz densities changing at unknown times...