We introduce a generalized Bayesian method for multiple changepoint analysis with a loss function inspired by multinomial logistic regression. The method does not require a specification of the data-generating process and avoids restrictive assumptions on the nature of changepoints. From the joint posterior distribution, we can make simultaneous inference on the locations of changepoints and the coefficients of a multinomial logistic regression model for distinguishing data across homogeneous segments. The multinomial logistic regression coefficients provide a familiar means of interpreting potentially complex changes. To select the number of changepoints, we leverage posterior summaries that measure whether the multinomial logistic classifier can distinguish data from either side of a potential changepoint. To simulate from the generalized posterior distribution, we present a Gibbs sampler based on Pólya-Gamma data augmentation. We assess the accuracy and flexibility of our method through simulation studies featuring different types of changes and demonstrate its interpretability through applications to financial network data and topological data derived from nanoparticle videos.

翻译：本文提出一种基于多项逻辑回归损失函数的广义贝叶斯多重变点分析方法。该方法无需指定数据生成过程，且避免对变点本质施加限制性假设。通过联合后验分布，我们可同时推断变点位置及区分同质数据段的多项逻辑回归模型系数。多项逻辑回归系数为解释潜在复杂变化提供了直观途径。为选择变点数量，我们利用后验统计量评估多项逻辑分类器能否区分潜在变点两侧的数据。为从广义后验分布中采样，我们提出基于波利亚伽马数据增强的吉布斯采样器。通过涵盖不同变化类型的模拟研究验证了该方法的准确性与灵活性，并在金融网络数据及纳米颗粒视频拓扑数据中展示了其可解释性。