Journal article
Bayesian changepoint detection via logistic regression and the topological analysis of image series
Technometrics, Vol.67(4), pp.693-705
10/02/2025
DOI: 10.1080/00401706.2025.2515928
Abstract
We present a Bayesian method for multivariate changepoint detection that allows for simultaneous inference on the location of a changepoint and the coefficients of a logistic regression model for distinguishing pre-changepoint data from post-changepoint data. In contrast to many methods for multivariate changepoint detection, the proposed method is applicable to data of mixed type and avoids strict assumptions regarding the distribution of the data and the nature of the change. The regression coefficients provide an interpretable description of a potentially complex change. For posterior inference, the model admits a simple Gibbs sampling algorithm based on Pólya-gamma data augmentation. We establish conditions under which the proposed method is guaranteed to recover the true underlying changepoint. As a testing ground for our method, we consider the problem of detecting topological changes in time series of images. We demonstrate that our proposed method BCLR, combined with a topological feature embedding, performs well on both simulated and real image data. The method also successfully recovers the location and nature of changes in more traditional changepoint tasks. An implementation of our method is available in the Python package bclr.
Details
- Title: Subtitle
- Bayesian changepoint detection via logistic regression and the topological analysis of image series
- Creators
- Andrew M Thomas - University of Iowa, Statistics and Actuarial ScienceMichael Jauch - Florida State UniversityDavid S. Matteson - Cornell University
- Resource Type
- Journal article
- Publication Details
- Technometrics, Vol.67(4), pp.693-705
- DOI
- 10.1080/00401706.2025.2515928
- ISSN
- 0040-1706
- eISSN
- 1537-2723
- Publisher
- Taylor & Francis; PHILADELPHIA
- Grant note
- NSF: DMS-2114143
The authors gratefully acknowledge funding from NSF grant DMS-2114143
- Language
- English
- Electronic publication date
- 06/06/2025
- Date published
- 10/02/2025
- Academic Unit
- Statistics and Actuarial Science
- Record Identifier
- 9984828432102771
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