This text introduces general state space models in detail before focusing on dynamic linear models, emphasizing their Bayesian analysis. It illustrates all the fundamental steps needed to use dynamic linear models in practice, using R.

This book is on Dynamic Linear Models (DLMs) and the development of an R package to analyze them.

Fully worked-out examples in the freely available statistical software R Guides the reader in a friendly way from the basics of the Bayesian approach to its practical application to time series analysis Coverage includes advanced Bayesian computations, Markov chain Monte Carlo methods, and particle filters Includes supplementary material: sn.pub/extras

Inhalt
Introduction: basic notions about Bayesian inference.- Dynamic linear models.- Model specification.- Models with unknown parameters.- Sequential Monte Carlo methods.