Bayesian Change Point Detection with Spike-and-Slab Priors
研究了使用尖峰-平板先验来一致估计变点数量和位置，提出了一种快速贝叶斯变点检测方法，在数值实验中比现有方法更稳健。
We study the use of spike-and-slab priors for consistent estimation of the number of change points and their locations. Leveraging recent results in the variable selection literature, we show that an estimator based on spike-and-slab priors achieves optimal localization rate in the multiple offline change point detection problem. Based on this estimator, we propose a Bayesian change point detection method, which is one of the fastest Bayesian methodologies. We demonstrate through empirical work the good performance of our approach vis-a-vis some state-of-the-art benchmarks. Interestingly, despite having a Gaussian noise assumption, our approach is more robust to misspecification of the error terms than the competing methods in numerical experiments. Supplementary materials for this article are available online.