Objective Bayesian Analysis for Change Point Problems
This work addresses statistical modeling for change point detection, which is incremental as it builds on existing loss-based methods.
The paper tackles change point analysis by introducing a loss-based approach for both known and unknown numbers of change points, demonstrating its performance on simulated and real data sets.
In this paper we present a loss-based approach to change point analysis. In particular, we look at the problem from two perspectives. The first focuses on the definition of a prior when the number of change points is known a priori. The second contribution aims to estimate the number of change points by using a loss-based approach recently introduced in the literature. The latter considers change point estimation as a model selection exercise. We show the performance of the proposed approach on simulated data and real data sets.