Bayesian Inference for High-dimensional Time Series with a Stationary Directed Acyclic Graphical Structure

In multivariate time series analysis, understanding the underlying causal relationships among variables is often of interest for various applications. Directed acyclic graphs (DAGs) provide a powerful framework for representing causal dependencies. This paper proposes a novel Bayesian approach for m