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CE_09C Sun, 8/3/2014, 8:30 AM - 5:00 PM CC-161
Bayesian Dynamic Models: Time Series Analysis and Forecasting — Professional Development Continuing Education Course
ASA , Section on Bayesian Statistical Science
This short-course covers basic principles and methods of Bayesian dynamic modeling in time series analysis and forecasting, with methodological details of central model classes explored in a range of examples. A main focus is on dynamic linear models and related methods of inference and forecasting, including multivariate time series analysis. Links between time and frequency domain, and stationary time series models, will be covered, as well as selected developments in nonlinear and non-Gaussian dynamic models and associated Monte Carlo Markov chain simulation methods for analysis. The course will conclude by contacting some some recent modeling and applied developments in multivariate time series and forecasting. The course draws on a range of examples and case studies from business, finance, signal processing and the biomedical sciences. The course material will be accessible to advanced students, academics and/or professionals with strong statistical modelling backgrounds and prior exposure to essentials of Bayesian analysis. Familiarity with- and working facility in- multivariate distribution theory and statistical inference are prerequisites. Prior exposure to some areas of time series analysis will be useful though is not necessary.
Instructor(s): Raquel Prado, University of California, Santa Cruz, Mike West, Duke University



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