  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
mean-shift change point detection algorithm that makes use of repetition in
segment parameters, by introducing segment class labels that utilise a
Dirichlet process prior. The performance of the proposed approach was assessed
on both synthetic and real world data, highlighting the enhanced performance
when using parameter labelling.
