BayesCPclust: A Bayesian Approach for Clustering Constant-Wise Change-Point Data
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866917918036459520 |
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| author | da Cruz, Ana Carolina de Souza, Camila P. E. |
| author_facet | da Cruz, Ana Carolina de Souza, Camila P. E. |
| contents | 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 simultaneously performing multiple change-point estimation. Additionally, our approach controls the number of clusters in the model, not requiring the specification of the number of clusters a priori. Satisfactory clustering and estimation results were obtained when evaluating our method under various simulated scenarios and on a real dataset from single-cell genomic sequencing. Our proposed methodology is implemented as an R package called BayesCPclust and is available from the Comprehensive R Archive Network at https://CRAN.R-project.org/package=BayesCPclust. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2305_17631 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | BayesCPclust: A Bayesian Approach for Clustering Constant-Wise Change-Point Data da Cruz, Ana Carolina de Souza, Camila P. E. Methodology 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 simultaneously performing multiple change-point estimation. Additionally, our approach controls the number of clusters in the model, not requiring the specification of the number of clusters a priori. Satisfactory clustering and estimation results were obtained when evaluating our method under various simulated scenarios and on a real dataset from single-cell genomic sequencing. Our proposed methodology is implemented as an R package called BayesCPclust and is available from the Comprehensive R Archive Network at https://CRAN.R-project.org/package=BayesCPclust. |
| title | BayesCPclust: A Bayesian Approach for Clustering Constant-Wise Change-Point Data |
| topic | Methodology |
| url | https://arxiv.org/abs/2305.17631 |