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高维矩阵值时间序列的贝叶斯动态因子模型AI 解读

We introduce a class of Bayesian dynamic factor models for matrix-valued time series, with autoregressive factor dynamics and idiosyncratic components that allow stochastic volatility, outliers, and a Kronecker-structured covariance capturing cross-row and cross-column correlation. Exploiting the matrix structure, we make these richly parameterized models tractable in high dimensions and develop an efficient Gibbs sampler for estimation. For model comparison, we propose a…

用于动态有效连接的贝叶斯时变张量向量自回归模型AI 解读

In contemporary neuroscience, a key area of interest is dynamic effective connectivity, which is crucial for understanding the dynamic interactions and causal relationships between different brain regions. Dynamic effective connectivity can provide insights into how brain network interactions are altered in neurological disorders such as dyslexia. Time-varying vector autoregressive (TV-VAR) models have been employed to draw inferences for this purpose. However, their…