Bayesian Forecasting and Dynamic Models (Springer Series in Statistics) | 
 enlarge | Authors: Mike West, Jeff Harrison
Publisher: Springer
Category: Book
List Price: $115.00
Buy New: $70.30
You Save: $44.70 (39%)
New (16) Used (12) from $70.30
Rating:  2 reviews
Sales Rank: 452829
Media: Hardcover
Edition: 2nd
Pages: 680
Number Of Items: 1
Shipping Weight (lbs): 2.5
Dimensions (in): 9.3 x 6.5 x 1.8
ISBN: 0387947256
Dewey Decimal Number: 519.55
EAN: 9780387947259
Publication Date: March 26, 1999
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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 in the first edition, the second edition includes many more exercises and covers new topics at the research and application frontiers of Bayesian forecastings.
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| Customer Reviews:
time series using the Bayesian approach May 31, 2008
Michael R. Chernick (Holland PA)
36 out of 36 found this review helpful
A Bayesian approach is a natural way to deal with time series data. You construct a model based on past data and prior information and use the model to predict future values in the series. When the new observations come in the model can be updated (model parameters reestimated) and forecasts can be updated. Most of the time series literature deals with the classical (frequentist) approach incluing the well-known book by Box and Jenkins on forecasting and control. This book provides a mathematically rigorous treament of time series modeling based on a Bayesian approach. Many common forecasting procedures including the Kalman filter are iterative algorithms that could be derived as solutions for forecasting based on a Bayesian model of the time series.
This is the best text available on this topic.
A really good way to master Dinamic linear models May 21, 2001
Jose A. Sanchez Villanueva (Madrid (Spain))
11 out of 11 found this review helpful
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.
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