Overview
- 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
Part of the book series: Use R! (USE R)
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Table of contents (5 chapters)
Keywords
About this book
State space models have gained tremendous popularity in recent years in as disparate fields as engineering, economics, genetics and ecology. After a detailed introduction to general state space models, this book focuses on dynamic linear models, emphasizing their Bayesian analysis. Whenever possible it is shown how to compute estimates and forecasts in closed form; for more complex models, simulation techniques are used. A final chapter covers modern sequential Monte Carlo algorithms.
The book illustrates all the fundamental steps needed to use dynamic linear models in practice, using R. Many detailed examples based on real data sets are provided to show how to set up a specific model, estimate its parameters, and use it for forecasting. All the code used in the book is available online.
No prior knowledge of Bayesian statistics or time series analysis is required, although familiarity with basic statistics and R is assumed.
Bibliographic Information
Book Title: Dynamic Linear Models with R
Authors: Patrizia Campagnoli, Sonia Petrone, Giovanni Petris
Series Title: Use R!
DOI: https://doi.org/10.1007/b135794
Publisher: Springer New York, NY
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer-Verlag New York 2009
Softcover ISBN: 978-0-387-77237-0Published: 02 June 2009
eBook ISBN: 978-0-387-77238-7Published: 12 June 2009
Series ISSN: 2197-5736
Series E-ISSN: 2197-5744
Edition Number: 1
Number of Pages: XIII, 252
Topics: Statistical Theory and Methods