Bayesian time series analysis and forecasting with dynamic models

This short course covers models and methods for time series analysis using Bayesian dynamic models. The main focus will be on dynamic linear models (DLMs). Model building and well as Bayesian inference and forecasting within the class of univariate DLMs will be discussed in detail. The use of these models for time series analysis and forecasting will be illustrated using a wide range of examples from neuroscience, environmetrics and econometrics. Extensions to more sophisticated dynamic models, as well as MCMC methods for inference in such modeling settings, will also be discussed.