论文标题
基于事件的多试集合数据的贝叶斯信息标准
Bayesian Information Criterion for Event-based Multi-trial Ensemble data
论文作者
论文摘要
在许多科学领域（如神经科学和气象学）中，瞬时重复现象无处不在。时间不基质的矢量自回旋模型（VAR）可用于表征与此类现象相关的事件系统动力学的表征，并且可以通过利用多维数据收集系统演化的多个时间窗口演化的样品来学习，每种系统都与瞬时现象相关联，我们将呼吁“试用”。但是，通常依赖于Akaike或贝叶斯信息标准（AIC/BIC）的最佳VAR模型选择方法通常不是为多试验数据而设计的。在这里，我们得出了在检测事件后收集的多试集合数据的BIC方法。我们使用模拟双变量AR模型显示，多试验BIC能够恢复真实的模型顺序。我们还通过模拟的瞬态事件和实际数据证明，多试验BIC能够估计动态系统建模的足够小的模型顺序。
Transient recurring phenomena are ubiquitous in many scientific fields like neuroscience and meteorology. Time inhomogenous Vector Autoregressive Models (VAR) may be used to characterize peri-event system dynamics associated with such phenomena, and can be learned by exploiting multi-dimensional data gathering samples of the evolution of the system in multiple time windows comprising, each associated with one occurrence of the transient phenomenon, that we will call "trial". However, optimal VAR model order selection methods, commonly relying on the Akaike or Bayesian Information Criteria (AIC/BIC), are typically not designed for multi-trial data. Here we derive the BIC methods for multi-trial ensemble data which are gathered after the detection of the events. We show using simulated bivariate AR models that the multi-trial BIC is able to recover the real model order. We also demonstrate with simulated transient events and real data that the multi-trial BIC is able to estimate a sufficiently small model order for dynamic system modeling.
