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Bayesian Modeling Using WinBUGS [Anglais] [Relié]

Ioannis Ntzoufras

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Présentation de l'éditeur

A hands–on introduction to the principles of Bayesian modeling using WinBUGS Bayesian Modeling Using WinBUGS provides an easily accessible introduction to the use of WinBUGS programming techniques in a variety of Bayesian modeling settings. The author provides an accessible treatment of the topic, offering readers a smooth introduction to the principles of Bayesian modeling with detailed guidance on the practical implementation of key principles. The book begins with a basic introduction to Bayesian inference and the WinBUGS software and goes on to cover key topics, including: Markov Chain Monte Carlo algorithms in Bayesian inference Generalized linear models Bayesian hierarchical models Predictive distribution and model checking Bayesian model and variable evaluation Computational notes and screen captures illustrate the use of both WinBUGS as well as R software to apply the discussed techniques. Exercises at the end of each chapter allow readers to test their understanding of the presented concepts and all data sets and code are available on the book′s related Web site. Requiring only a working knowledge of probability theory and statistics, Bayesian Modeling Using WinBUGS serves as an excellent book for courses on Bayesian statistics at the upper–undergraduate and graduate levels. It is also a valuable reference for researchers and practitioners in the fields of statistics, actuarial science, medicine, and the social sciences who use WinBUGS in their everyday work.

Quatrième de couverture

A hands–on introduction to the principles of Bayesian modeling using WinBUGS Bayesian Modeling Using WinBUGS provides an easily accessible introduction to the use of WinBUGS programming techniques in a variety of Bayesian modeling settings. The author provides an accessible treatment of the topic, offering readers a smooth introduction to the principles of Bayesian modeling with detailed guidance on the practical implementation of key principles. The book begins with a basic introduction to Bayesian inference and the WinBUGS software and goes on to cover key topics, including: Markov Chain Monte Carlo algorithms in Bayesian inference Generalized linear models Bayesian hierarchical models Predictive distribution and model checking Bayesian model and variable evaluation Computational notes and screen captures illustrate the use of both WinBUGS as well as R software to apply the discussed techniques. Exercises at the end of each chapter allow readers to test their understanding of the presented concepts and all data sets and code are available on the book′s related Web site. Requiring only a working knowledge of probability theory and statistics, Bayesian Modeling Using WinBUGS serves as an excellent book for courses on Bayesian statistics at the upper–undergraduate and graduate levels. It is also a valuable reference for researchers and practitioners in the fields of statistics, actuarial science, medicine, and the social sciences who use WinBUGS in their everyday work.

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Couverture | Copyright | Table des matières | Extrait | Index | Quatrième de couverture
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Amazon.com: 4.3 étoiles sur 5  7 commentaires
19 internautes sur 19 ont trouvé ce commentaire utile 
5.0 étoiles sur 5 Well Done 1 octobre 2009
Par Philip Turk - Publié sur Amazon.com
Format:Relié|Achat vérifié
I try to read at least a couple of statistics books every year and this was one of them for 2009. So far I have been really impressed. If you want a complete introduction to Bayesian statistics, then buy this book. You will find a balanced blend of theory, applications, and the use of the WinBUGS software package all under one roof. The book's treatment of models for count data is notable. Ntzoufras has a nice way of expressing himself that makes the reading move along. I would have no compunction at all about using this book to teach a M.S.-level course for statistics majors. If you are an ecologist, say, then you should probably have both a probability and mathematical statistics course under your belt to fully absorb all that is going on.
16 internautes sur 16 ont trouvé ce commentaire utile 
5.0 étoiles sur 5 Excellent Intro both to Bayesian and WinBUGS 2 juin 2009
Par Byron Hall - Publié sur Amazon.com
Format:Relié
I highly recommend this book to anyone interested in Bayesian statistics and the WinBUGS software. Examples are excellent and clear, even the examples of code, which are well-commented. If I had to pick at it, there are a few typos, but then again, I got the first printing, and they are noted on the author's website. I have read many Bayesian books, and if you are new, I think this is one of the best.
11 internautes sur 12 ont trouvé ce commentaire utile 
5.0 étoiles sur 5 This is THE definitive text for learning THE Bayesian statistics inference tool, WinBUGS. Bravo! 31 août 2010
Par John V. Karavitis - Publié sur Amazon.com
Format:Relié
John V. Karavitis Once you've learned probability and statistics, either at the high school or college level, you are obliged to delve into Bayesian statistics. Regardless of the perennial argument between the frequentist and Bayesian paradigms, Bayesian statistics has found its way into all fields of knowledge, from biology to cosmology to ecology and beyond. The explosion in computing power has taken Bayesian statistics from a curious philosophical approach to statistics into THE way of dealing with unknowns, of dealing with data. In this book, Dr. Ntzoufras has done a great service to all of science by providing what I believe is the definitive text on how to use WinBUGS. Chapter 1 starts off with a general review of Bayesian statistics, and just an overview - don't even think about tackling this book without a thorough year's worth of probability and statistics. Chapter 2 dives into Markov Chain Monte Carlo methods, the meat and potatoes of applying Bayesian statistics. After that, it's pure WinBUGS, where you get actual problems and screenshots of the software, in chapters that address various models, e.g. generalized linear models, etc. You simply cannot get away with ignoring Bayesian statistics anymore; again, the explosion in computing power, the lack of which was the one obstacle to properly implementing and utilizing this epistemological paradigm, finally allows one to gain technical proficiency in applying Bayesian statistics. WinBUGS is a must-have item in one's research and data analysis toolkit. If anything, I would ask Dr. Nztoufras to create YouTube vid clips that walk people through ALL of his examples, it would be a great addition to an already stellar textbook. Read about WinBUGS at home, and listen to it on the way to work! Five stars! John V. Karavitis, John Karavitis, Karavitis
5 internautes sur 5 ont trouvé ce commentaire utile 
4.0 étoiles sur 5 Luxury 29 juillet 2012
Par Dimitri Shvorob - Publié sur Amazon.com
Format:Relié
Here we are - a not-especially-monumental statistics book with a $100-plus price tag. I get a negative bias right there, but on reflection, I grudgingly admit that the product justifies the price.

Just to be clear, this isn't a WinBUGS "bible" - not when BUGS/WinBUGS comes with online user guides and three volumes of examples; you will need to consult the documentation for arcane things like complex models, and for simple things like BUGS error messages. (If you use R, you will also want to read the doc for R2WinBUGS package. Ntzoufras has R code in first chapters, but when he gets to WinBUGS, GUI interface alone is used).

Nor is it the only book introducing WinBUGS: "Doing Bayesian data analysis" by John Kruschke and "Data analysis using regression and multilevel/hierarchical models" by Andrew Gelman and Jennifer Hill both do that quite well.

It is, however, the only book that effectively uses WinBUGS to provide a hands-on and wide-ranging tour of Bayesian statistics, and thus makes for an excellent, substantial and friendly introduction to Bayesian methods. ("Hands-on" is the operative word; John Kruschke's book, for example, is very nice, but, by taking a more theoretical approach, does not engage the reader quite as effectively).

In summary, if your interest is primarily in WinBUGS, free online references are the way to go, but if you are looking for a good, get-hands-dirty introduction to Bayesian methods, this is your best bet.
3 internautes sur 3 ont trouvé ce commentaire utile 
5.0 étoiles sur 5 Exemplary Introduction to Bayesian Statistical Inference 10 mars 2010
Par R. Wetzels - Publié sur Amazon.com
Format:Relié
I recommend this book to any researcher interested in Bayesian modeling using WinBUGS. The book is clearly written, covers a wide range of important statistical models, and -most importantly- illuminates theory with concrete examples implemented in WinBUGS. Together with EJ Wagenmakers, I have written a review that is now in press for the Journal of Mathematical Psychology. Please see [...]
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