Preprint
Bayesian changepoint detection via logistic regression and the topological analysis of image series
ArXiv.org
Cornell University
01/05/2024
DOI: 10.48550/arxiv.2401.02917
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 P\'olya-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 the proposed method, combined with a
novel topological feature embedding, performs well on both simulated and real
image data.
Details
- Title: Subtitle
- Bayesian changepoint detection via logistic regression and the topological analysis of image series
- Creators
- Andrew M ThomasMichael JauchDavid S Matteson
- Resource Type
- Preprint
- Publication Details
- ArXiv.org
- DOI
- 10.48550/arxiv.2401.02917
- ISSN
- 2331-8422
- Publisher
- Cornell University
- Language
- English
- Date posted
- 01/05/2024
- Academic Unit
- Statistics and Actuarial Science
- Record Identifier
- 9984543458702771
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