JSM 2012 Online Program

The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.

Online Program Home


CE_16C	Mon, 7/30/2012, 8:30 AM - 5:00 PM	HQ-Indigo 202
Bayesian Time Series Analysis and Forecasting: Models and Methods — Continuing Education Course
ASA , Section on Bayesian Statistical Science
Instructor(s): Raquel Prado , University of California at Santa Cruz, Mike West, Duke University
The course covers aspects of the theory, methodology and computation for Bayesian time series analysis and forecasting. A main focus is on time-varying parameter state-space models, building on the essential foundations of model development and Bayesian learning in classes of dynamic linear models. We then explore a number of applied topics in multivariate time series analysis, and some nonlinear and non-Gaussian dynamic models. Simulation-based methods for filtering, parameter learning and smoothing are key. We draw on multiple examples and case studies from business, finance, climatology, signal processing and the biomedical sciences, with live data analyses using software (R and Matlab) of the authors. The course targets students or professionals with strong statistical background and prior exposure to the essentials of Bayesian analysis. Prior exposure to time series analysis is useful though not necessary.

2012 JSM Online Program Home

For information, contact jsm@amstat.org or phone (888) 231-3473.

If you have questions about the Continuing Education program, please contact the Education Department.