Title: Bayes Watch: Bayesian Change-point Detection for Process Monitoring with Fault Detection Show Chinese title

Title: 贝叶斯监视：用于故障检测的过程监控中的贝叶斯变化点检测

Abstract: When a predictive model is in production, it must be monitored in real-time to ensure that its performance does not suffer due to drift or abrupt changes to data. Ideally, this is done long before learning that the performance of the model itself has dropped by monitoring outcome data. In this paper we consider the problem of monitoring a predictive model that identifies the need for palliative care currently in production at the Mayo Clinic in Rochester, MN. We introduce a framework, called \textit{Bayes Watch}, for detecting change-points in high-dimensional longitudinal data with mixed variable types and missing values and for determining in which variables the change-point occurred. Bayes Watch fits an array of Gaussian Graphical Mixture Models to groupings of homogeneous data in time, called regimes, which are modeled as the observed states of a Markov process with unknown transition probabilities. In doing so, Bayes Watch defines a posterior distribution on a vector of regime assignments, which gives meaningful expressions on the probability of every possible change-point. Bayes Watch also allows for an effective and efficient fault detection system that assesses what features in the data where the most responsible for a given change-point. Show Chinese abstract

Abstract: 当预测模型投入生产时，必须进行实时监控，以确保其性能不会因数据漂移或数据的突然变化而下降。 理想情况下，这是在发现模型本身的性能下降之前就进行的，通过对结果数据进行监控。 在本文中，我们考虑了监控一个预测模型的问题，该模型目前在明尼苏达州罗切斯特的梅奥诊所投入生产。 我们引入了一个称为\textit{贝叶斯观察}的框架，用于在具有混合变量类型和缺失值的高维纵向数据中检测变化点，并确定变化点发生在哪些变量上。 贝叶斯 Watch 为时间上的同质数据分组（称为制度）拟合一系列高斯图混合模型，这些制度被建模为具有未知转移概率的马尔可夫过程的观察状态。 在此过程中， Bayes Watch 在制度分配向量上定义了一个后验分布，这给出了每个可能变化点的概率的有意义表达。 Bayes Watch 还允许一种有效且高效的故障检测系统，该系统评估数据中哪些特征最可能导致给定的变化点。