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In this work we introduce a semi-parametric Bayesian change-point model,defining its time dynamic as a latent Markov process based on the Dirichletprocess. We treat the number of change point as a random variable and weestimate it during model fitting. Posterior inference is carried out using aMarkov chain Monte Carlo algorithm based on a marginalized version of theproposed model. The model is illustrated using simulated examples and two realdatasets, namely the coal- mining disasters, that is a widely used dataset forillustrative purpose, and a dataset of indoor radon recordings. With thesimulated examples we show that the model is able to recover the parameters andnumber 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 pointsidentification. The results obtained on the coal-mining disasters and radondata are coherent with previous literature.

Semi-parametric Bayesian change-point model based on the Dirichlet process