A Bayesian Approach for Clustering Constant-wise Change-point Data

05/28/2023

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by   Ana Carolina da Cruz, et al.

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Change-point models deal with ordered data sequences. Their primary goal is to infer the locations where an aspect of the data sequence changes. In this paper, we propose and implement a nonparametric Bayesian model for clustering observations based on their constant-wise change-point profiles via Gibbs sampler. Our model incorporates a Dirichlet Process on the constant-wise change-point structures to cluster observations while performing change-point estimation simultaneously. Additionally, our approach controls the number of clusters in the model, not requiring the specification of the number of clusters a priori. Our method's performance is evaluated on simulated data under various scenarios and on a publicly available single-cell copy-number dataset.