# Supplementary Notes: Segment Parameter Labelling in MCMC Change Detection

## TL;DR

This paper introduces a Bayesian change point detection method that leverages segment parameter repetition using a Dirichlet process prior to improve segmentation accuracy in time series data.

## Contribution

It proposes a novel Bayesian algorithm that incorporates segment class labels with a Dirichlet process prior to exploit parameter patterns across segments.

## Key findings

- Enhanced change point detection accuracy.
- Effective utilization of segment parameter repetition.
- Demonstrated improvements over traditional methods.

## Abstract

This work addresses the problem of segmentation in time series data with respect to a statistical parameter of interest in Bayesian models. It is common to assume that the parameters are distinct within each segment. As such, many Bayesian change point detection models do not exploit the segment parameter patterns, which can improve performance. This work proposes a Bayesian change point detection algorithm that makes use of repetition in segment parameters, by introducing segment class labels that utilise a Dirichlet process prior.