Bayesian Forecasting and Dynamic Models (Springer Series in Statistics) 2nd Edition by Mike West (PDF)

Description

This text is concerned with Bayesian learning, inference and forecasting in dynamic environments. We describe the structure and theory of classes of dynamic models and their uses in forecasting and time series analysis. The principles, models and methods of Bayesian forecasting and time – ries analysis have been developed extensively during the last thirty years. Thisdevelopmenthasinvolvedthoroughinvestigationofmathematicaland statistical aspects of forecasting models and related techniques. With this has come experience with applications in a variety of areas in commercial, industrial, scienti?c, and socio-economic ?elds. Much of the technical – velopment has been driven by the needs of forecasting practitioners and applied researchers. As a result, there now exists a relatively complete statistical and mathematical framework, presented and illustrated here. In writing and revising this book, our primary goals have been to present a reasonably comprehensive view of Bayesian ideas and methods in m- elling and forecasting, particularly to provide a solid reference source for advanced university students and research workers.

User’s Reviews

Editorial Reviews: From the Back Cover 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.

Reviews from Amazon users which were colected at the time this book was published on the website:

⭐Each topic is thoroughly covered with theoretical rigor. I wish the authors publish an applied book with numerical example, and have all the algorithms coded in R or MATLAB. Their earlier attempt of black-box software BATS was entirely outdated, and black-box software sucks for its lack of flexibility.

⭐In this book you will find a good theoretical approach to the construction of bayesian DLMs, together with all the needed guidelines to actually implement them.Recommended.

⭐There is a lot of useful information in this, but, by G**, extracting it is hard work. Not because of the math, but because of the writing: out of curiosity I calculated the Flesch metric for §1.4 and got a figure of 20, which was consistent with my informal impression. Just so you know what’s ahead of you.

⭐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.

⭐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.

⭐If you are interested in this subject this is a great place to start. I like the journal papers on this subject written by these authors as well.

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