标题： 一种用于在线检测渐变的动态变化模型 显示英文标题

标题： A Change Dynamic Model for the Online Detection of Gradual Change

摘要： 随机过程的统计性质的变化通常假设通过变点发生，这些变点标志着过程中行为完全且彻底改变的瞬间。 在变化过渡逐渐发生的情况下，这种假设可能导致识别和响应过程变化的能力降低。 考虑到这一观察结果，我们在贝叶斯框架中引入了一种新的动态变化模型，用于在线检测渐变情况，其中变点在分层模型中用于指示渐变开始或终止的时刻。 我们将该模型应用于合成数据以及癫痫发作期间记录的脑电图读数，发现我们的动态变化模型能够比传统的变点模型更快、更准确地识别渐变。 显示英文摘要

摘要： Changes in the statistical properties of a stochastic process are typically assumed to occur via change-points, which demark instantaneous moments of complete and total change in process behavior. In cases where these transitions occur gradually, this assumption can result in a reduced ability to properly identify and respond to process change. With this observation in mind, we introduce a novel change-dynamic model for the online detection of gradual change in a Bayesian framework, in which change-points are used within a hierarchical model to indicate moments of gradual change onset or termination. We apply this model to synthetic data and EEG readings drawn during epileptic seizure, where we find our change-dynamic model can enable faster and more accurate identification of gradual change than traditional change-point models allow.