Abstract
We discuss conditionally Gaussian dynamic linear models for analysis and forecasting of univariate time series and present simulation-based methods for Bayesian filtering and smoothing within this class of models, including Markov chain Monte Carlo (MCMC) and sequential Monte Carlo (SMC) methods. We illustrate the use of these models in several applied settings. We also present extensions to non-linear dynamic models, mixture state-space models and multi-process models.
Brief Biography
Raquel Prado is Professor in the Department of Statistics of the Baskin School of Engineering at the University of California Santa Cruz, where she has been on the faculty since 2001. In 1998 she graduated with a PhD in Statistics and Decision Sciences from Duke University. From 1998 until 2001 she was an Assistant Professor in Statistics at Universidad Simon Bolivar in Venezuela. She was a co-recipient of the Outstanding Statistical Application Award of the American Statistical Association in 1999. She is a Fellow of the American Statistical Association. She is also a Fellow and Past President of the International Society for Bayesian Analysis and currently a member of the Committee on Applied and Theoretical Statistics (CATS) of the National Academies of Science, Engineering and Medicine.