Department of Psychology, Princeton University, Princeton, NJ 08540, USA.
Neural Comput. 2010 Sep 1;22(9):2452-76. doi: 10.1162/NECO_a_00007.
Change-point models are generative models of time-varying data in which the underlying generative parameters undergo discontinuous changes at different points in time known as change points. Change-points often represent important events in the underlying processes, like a change in brain state reflected in EEG data or a change in the value of a company reflected in its stock price. However, change-points can be difficult to identify in noisy data streams. Previous attempts to identify change-points online using Bayesian inference relied on specifying in advance the rate at which they occur, called the hazard rate (h). This approach leads to predictions that can depend strongly on the choice of h and is unable to deal optimally with systems in which h is not constant in time. In this letter, we overcome these limitations by developing a hierarchical extension to earlier models. This approach allows h itself to be inferred from the data, which in turn helps to identify when change-points occur. We show that our approach can effectively identify change-points in both toy and real data sets with complex hazard rates and how it can be used as an ideal-observer model for human and animal behavior when faced with rapidly changing inputs.
变点模型是一种随时间变化的数据生成模型，其中潜在的生成参数在称为变点的不同时间点经历不连续的变化。变点通常代表潜在过程中的重要事件，例如反映在脑电图数据中的大脑状态变化，或反映在股票价格中的公司价值变化。然而，在嘈杂的数据流中，变点可能难以识别。以前使用贝叶斯推断在线识别变点的尝试依赖于提前指定它们发生的速率，称为危险率 (h)。这种方法导致的预测可能强烈依赖于 h 的选择，并且无法针对 h 随时间变化的系统进行最佳处理。在这封信中，我们通过为早期模型开发层次扩展来克服这些限制。这种方法允许从数据中推断 h 本身，这反过来有助于确定何时发生变点。我们表明，我们的方法可以有效地识别具有复杂危险率的玩具和真实数据集以及如何在面对快速变化的输入时将其用作人类和动物行为的理想观察者模型。
