  In this work we introduce a semi-parametric Bayesian change-point model,
defining its time dynamic as a latent Markov process based on the Dirichlet
process. We treat the number of change point as a random variable and we
estimate it during model fitting. Posterior inference is carried out using a
Markov chain Monte Carlo algorithm based on a marginalized version of the
proposed model. The model is illustrated using simulated examples and two real
datasets, namely the coal- mining disasters, that is a widely used dataset for
illustrative purpose, and a dataset of indoor radon recordings. With the
simulated examples we show that the model is able to recover the parameters and
number of change points, and we compare our results with the ones of the-state-
of-the-art models, showing a clear improvement in terms of change points
identification. The results obtained on the coal-mining disasters and radon
data are coherent with previous literature.
