论文标题
变更动态模型，用于在线检测逐渐变化
A Change Dynamic Model for the Online Detection of Gradual Change
论文作者
论文摘要
通常假定随机过程的统计特性的变化是通过变更点发生的，该变化点是完全变化的过程行为的瞬时瞬间。如果这些过渡逐渐发生，则此假设可能会导致正确识别和响应过程变化的能力降低。考虑到这一观察，我们引入了一种新颖的变化模型，用于在线检测贝叶斯框架中逐渐变化的模型，其中在层次模型中使用了变化点，以指示逐渐变化或终止的时刻。我们将此模型应用于癫痫发作期间绘制的合成数据和脑电图读数，在这里我们发现更改动力模型可以比传统的变更点模型允许的速度更快，更准确地识别渐进变化。
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.
