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Explore a comprehensive lecture on structured dynamic graphical models and scaling multivariate time series delivered by Professor Mike West from Duke University. Delve into recent research and development in dynamic statistical models for multivariate time series forecasting, addressing challenges of scalability and model complexity. Learn about the "Decouple/Recouple" strategy for coherent Bayesian analysis, Bayesian dynamic dependency networks (DDNs), and simultaneous graphical dynamic linear models (SGDLMs). Discover aspects of model specification, fitting, and computation, including importance sampling and variational Bayes methods for sequential analysis and forecasting. Examine applications in financial time series forecasting and portfolio decisions, highlighting the utility of these models. Gain insights into advances in Bayesian dynamic modeling and strategies for scalability to higher-dimensional "big, dynamic data" contexts.
