# Objective Bayesian Analysis for Change Point Problems

## TL;DR

This paper introduces a loss-based Bayesian method for change point analysis, addressing both prior specification with known change points and estimating their number as a model selection problem, validated on simulated and real data.

## Contribution

It presents a novel loss-based Bayesian framework for change point detection and model selection, expanding the methodological toolkit in this area.

## Key findings

- Effective on simulated data
- Successful application to real data
- Improves change point estimation accuracy

## Abstract

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.