Bayesian Forecasting and Dynamic Models (Springer Series in Statistics)

Features

• Cover Type: Hard Cover with 680 pages
• Published by: Springer
• Edition: 2nd Edition March 26, 1999
• Written in: English
• ISBN 10 Number: 0387947256
• ISBN 13 Number: 978-0387947259
• Book Dimensions: 9.3 x 6.5 x 1.8 inches
• Weighs: 2.5 pounds

Product Description
The second edition of this book includes revised, updated, and additional material on the structure, theory, and application of classes of dynamic models in Bayesian time series analysis and forecasting. In addition to wide ranging updates to central material, the second edition includes many more exercises and covers new topics at the research and application frontiers of Bayesian forecastings.

Reader Reviews
As a reader with an economical background, mathematical texts are usually hard to be followed. Nevertheless, dinamic models through bayesian forecasting are afordable with this book. Introductory chapters on the bayesian learning algorithm and univariate models rough out the kernel of the issue. Once you dive into the following more complicated chapters you can get lost but the main idea is got. To avoid getting lost, several readings are necessary. Finally, last chapters for non linear models, models with exponential distributions and MCMC methods are really heavy going but a light reading can allow you to get a general overview. All in all, is a great workbook. The main drawback may be the lack of more practical examples to illustrate the theoretical concepts.