Bayesian Statistical Modelling 2nd Edition by Peter Congdon (PDF)

Description

Bayesian methods combine the evidence from the data at hand with previous quantitative knowledge to analyse practical problems in a wide range of areas. The calculations were previously complex, but it is now possible to routinely apply Bayesian methods due to advances in computing technology and the use of new sampling methods for estimating parameters. Such developments together with the availability of freeware such as WINBUGS and R have facilitated a rapid growth in the use of Bayesian methods, allowing their application in many scientific disciplines, including applied statistics, public health research, medical science, the social sciences and economics. Following the success of the first edition, this reworked and updated book provides an accessible approach to Bayesian computing and analysis, with an emphasis on the principles of prior selection, identification and the interpretation of real data sets.The second edition:Provides an integrated presentation of theory, examples, applications and computer algorithms.Discusses the role of Markov Chain Monte Carlo methods in computing and estimation.Includes a wide range of interdisciplinary applications, and a large selection of worked examples from the health and social sciences.Features a comprehensive range of methodologies and modelling techniques, and examines model fitting in practice using Bayesian principles.Provides exercises designed to help reinforce the reader’s knowledge and a supplementary website containing data sets and relevant programs.Bayesian Statistical Modelling is ideal for researchers in applied statistics, medical science, public health and the social sciences, who will benefit greatly from the examples and applications featured. The book will also appeal to graduate students of applied statistics, data analysis and Bayesian methods, and will provide a great source of reference for both researchers and students.Praise for the First Edition:“It is a remarkable achievement to have carried out such a range of analysis on such a range of data sets. I found this book comprehensive and stimulating, and was thoroughly impressed with both the depth and the range of the discussions it contains.” – ISI – Short Book Reviews“This is an excellent introductory book on Bayesian modelling techniques and data analysis” – Biometrics“The book fills an important niche in the statistical literature and should be a very valuable resource for students and professionals who are utilizing Bayesian methods.” – Journal of Mathematical Psychology

User’s Reviews

Editorial Reviews: Review “This text is ideal for researchers in applied statistics, medical sciences, public health and the social sciences, who will benefit greatly from the examples and applications featured. The book will also appeal to graduate students of applied statistics, data analysis and Bayesian methods, and will provide a great source of reference for both researchers and students.” (Zentralblatt MATH, 2010) From the Inside Flap Bayesian methods combine the evidence from the data at hand with previous quantitative knowledge to analyse practical problems in a wide range of areas. The calculations were previously complex, but it is now possible to routinely apply Bayesian ;methods due to advances in computing technology and the use of new sampling methods for estimating parameters. Such developments together with the availability of freeware such as WINBUGS and R have facilitated a rapid growth in the use of Bayesian methods, allowing their application in many scientific disciplines, including applied statistics, public health research, medical science, the social sciences and economics. Following the success of the first edition, this reworked and updated book provides an accessible approach to Bayesian computing and analysis, with an emphasis on the principles of prior selection, identification and the interpretation of real data sets.The second edition: Provides an integrated presentation of theory, examples, applications and computer algorithms. Discusses the role of Markov Chain Monte Carlo methods in computing and estimation. Includes a wide range of interdisciplinary applications, and a large selection of worked examples from the health and social sciences. Features a comprehensive range of methodologies and modelling techniques, and examines model fitting in practice using Bayesian principles. Provides exercises designed to help reinforce the reader’s knowledge and a supplementary website containing data sets and relevant programs. Bayesian Statistical Modelling id ideal for researched in applied statistics, medical science, public health and the social sciences, who will benefit greatly from the examples and applications featured. The book will also appeal to graduate students of applied statistics, data analysis and Bayesian methods, and will provide a great source of reference for both researchers and students. Praise for the First Edition:”It is a remarkable achievement to have carried out such a rang of analysis on such a range of data sets. I found this book comprehensive and stimulating, and was thoroughly impressed with both the depth and the range of the discussions it contains.” – ISI – Short Book Reviews”This is an excellent introductory book on Bayesian modelling techniques and data analysis.” – Biometrics”The book fills an important niche in the statistical literature and should be a very valuable resource for students and professionals who are utilizing Bayesian methods.” – Journal of Mathematical Psychology From the Back Cover Bayesian methods combine the evidence from the data at hand with previous quantitative knowledge to analyse practical problems in a wide range of areas. The calculations were previously complex, but it is now possible to routinely apply Bayesian ;methods due to advances in computing technology and the use of new sampling methods for estimating parameters. Such developments together with the availability of freeware such as WINBUGS and R have facilitated a rapid growth in the use of Bayesian methods, allowing their application in many scientific disciplines, including applied statistics, public health research, medical science, the social sciences and economics. Following the success of the first edition, this reworked and updated book provides an accessible approach to Bayesian computing and analysis, with an emphasis on the principles of prior selection, identification and the interpretation of real data sets.The second edition: Provides an integrated presentation of theory, examples, applications and computer algorithms. Discusses the role of Markov Chain Monte Carlo methods in computing and estimation. Includes a wide range of interdisciplinary applications, and a large selection of worked examples from the health and social sciences. Features a comprehensive range of methodologies and modelling techniques, and examines model fitting in practice using Bayesian principles. Provides exercises designed to help reinforce the reader’s knowledge and a supplementary website containing data sets and relevant programs. Bayesian Statistical Modelling id ideal for researched in applied statistics, medical science, public health and the social sciences, who will benefit greatly from the examples and applications featured. The book will also appeal to graduate students of applied statistics, data analysis and Bayesian methods, and will provide a great source of reference for both researchers and students. Praise for the First Edition:”It is a remarkable achievement to have carried out such a rang of analysis on such a range of data sets. I found this book comprehensive and stimulating, and was thoroughly impressed with both the depth and the range of the discussions it contains.” – ISI – Short Book Reviews”This is an excellent introductory book on Bayesian modelling techniques and data analysis.” – Biometrics”The book fills an important niche in the statistical literature and should be a very valuable resource for students and professionals who are utilizing Bayesian methods.” – Journal of Mathematical Psychology About the Author Peter Congdon is Research Professor of Quantitative Geography and Health Statistics at Queen Mary University of London. He has written three earlier books on Bayesian modelling and data analysis techniques with Wiley, and has a wide range of publications in statistical methodology and in application areas. His current interests include applications to spatial and survey data relating to health status and health service research. His recent publications include work associated with the British Historical GIS Project (University of Portsmouth) and international collaborative work on psychiatric admissions in London and New York. Read more

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

⭐The book is simply awesome – it covers a very wide range of topics with pretty in-depth discussion in a practical way. Combined with the freely available WinBUGS programs on the author’s site, this would be pretty hard to beat. I have read quite a few Bayesian analysis books and this is definitely one of the best or simply the best. Looking forward to reading the author’s latest book just published.

⭐One of the reasons that I am giving this book such a low review is that it’s not clear from the book’s title, preface, or blurb on the back cover, nor from the first chapter what the purpose of this book and its intended audience are. The back cover is outright misleading; it calls this book an “introductory book on Bayesian modeling techniques”. In my opinion, this book seems to be aimed at researchers who already have a strong mastery of most of the techniques used in this book and want a comprehensive overview of the literature as well as a philosophically-sound guide of how to put this theory to use.The book is quite well-written. The prose is clear, and the author uses just the right amount of mathematical notation and graphs. The author has a comprehensive understanding of the literature, and gives numerous and appropriate references both to justify his points, and to point the reader to further reading.My objection to this book is that it is not a good place to go to learn any of the material–especially the theoretical material. There is not much exposition of the theory; in contrast to many books, this book provides a good justification of the “why” but a poor explanation of the “how”. While I deeply appreciate the “why”, I am not satisfied without both. One can have a fairly solid general background in statistics and yet still have trouble understanding this book: this book requires a solid prior background in Bayesian inference, MCMC sampling, and the appropriate areas of regression. This book would not be very useful to people who did not already know most of the material contained in it.In my opinion, this book would be greatly improved by being more honest and forward about the purpose, intended audience, and required background. I would also deeply appreciate it if the authors would do a better job of pointing to references which are better places to learn this material–most of the references are to the primary literature, and although they do reference a few very good textbooks, there are a ton of key subjects for which they do not point the reader to any good learning sources. I might be convinced to give this book five stars if the author could address these shortcomings.

⭐Congdon presents a very nice and modern treatment of Bayesian methods and models emphasizing implementation using BUGS or WINBUGS. The book covers Bayesian models for regression including linear, log-linear, robust and nonparametric regression. Covers association and classification, mixture models, latent variables, problems of missing data, survival analysis, hierarchical models for pooling information, time series and other correlated data methods (e.g. spatial processes), multivariate analysis, growth curves and model assessment criteria.The book is loaded with techniques and applications covering a wide variety of topics with reasonable depth.It also has a very large bibliography with many very relevant and useful references. But there is also a negative side to the bibliography. It was not carefully proofread and there are some annoyances as you will see the same reference listed two, three or more times in the bibliography. Also for such a nice reference text it should have included an author index as well as an ordinary index.Gibbs sampling is one of the primary estimation techniques in the book but the details are put off until section 10.1 where we get a nice introduction to Gibbs sampling and also the Metropolis algorithm with several excellent references.This is a good book to start implementing Bayesian methods through the MCMC technique. It contains mostly medical applications which is a nice feature for biostatisticians.

⭐Peter Congdon has a new fan. I decided to see the book after reading his more recent “Applied Bayesian hierarchical methods”, and found it similar in style. Once again, I find cosmetic blemishes and a somewhat dry writing – notably, while ABHM showed a large number of BUGS implementations, albeit moved them to out-of-sight appendices, here BUGS makes only a few appearances – outweighed by substance: the book presents a wide survey of Bayesian applications, with hundreds of papers in its references. This is not a book for beginners – whom I would point to the books by Kruschke and Ntzoufras – but a valuable resource for more advanced readers.

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