We present an efficient hierarchical model for inferring latent structure underlying multiple non-stationary time series. The proposed model describes the time-varying behavior of multiple time series in the partial autocorrelation domain, which results in a lower dimensional representation, and consequently computationally faster inference, than those required by models in the time and/or frequency domains, such as time-varying autoregressive models, which are commonly used in practice. We illustrate the performance of the proposed hierarchical dynamic PARCOR models and corresponding Bayesian inferential procedures in the context of analyzing multiple brain signals recorded simultaneously during specific experimental settings or clinical studies. The proposed approach allows us to efficiently obtain posterior summaries of the time-frequency characteristics of the multiple time series, as well as those summarizing their common underlying structure.

中文翻译：

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用于分析多个脑信号的分层动态 PARCOR 模型

我们提出了一种有效的层次模型，用于推断多个非平稳时间序列的潜在结构。所提出的模型描述了部分自相关域中多个时间序列的时变行为，与时域和/或频域（例如时间）中的模型所需的模型相比，这导致了更低维度的表示，因此计算上更快的推理- 变化自回归模型，在实践中常用。我们在分析特定实验设置或临床研究期间同时记录的多个脑信号的背景下，说明了所提出的分层动态 PARCOR 模型和相应的贝叶斯推理程序的性能。