Bayesian online learning of the hazard rate in change-point problems.
Bayesian online learning of the hazard rate in change-point problems.
复制标题
变点问题中风险率的贝叶斯在线学习。
DOI：
10.1162/neco_a_00007
复制
发表时间：
2010
影响因子：
2.9
通讯作者：
Gold,JoshuaI
中科院分区：
文献类型：
--
作者：
Wilson,RobertC;Nassar,MatthewR;Gold,JoshuaI
变化点模型是时变数据的生成模型，其中基础生成参数在称为变化点的不同时间点经历不连续的变化。变化点通常代表基础过程中的重要事件，例如脑电图数据反映的大脑状态的变化或股价反映的公司价值的变化。 然而，在嘈杂的数据流中很难识别变化点。 以前使用贝叶斯推理在线识别变化点的尝试依赖于提前指定变化点发生的速率，称为危险率 (h)。这种方法导致的预测在很大程度上取决于手的选择，无法最佳地处理时间不恒定的系统。在这封信中，我们通过开发早期模型的分层扩展来克服这些限制。这种方法允许从数据中推断出自己，这反过来又有助于识别变化点何时发生。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中的变化点，以及在面对快速变化的输入时如何将其用作人类和动物行为的理想观察者模型。
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 ofhand is unable to deal optimally with systems in whichhis not constant in time. In this letter, we overcome these limitations by developing a hierarchical extension to earlier models. This approach allowshitself 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.