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The BUGS Book: A Practical Introduction to Bayesian Analysis (Texts in Statistical Science (Chapman & Hall/CRC)) (Englisch) Taschenbuch – 15. Juni 2010


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Pressestimmen

"This is a beautiful book-it was a pleasure, and indeed great fun to read. ... The authors succeeded in writing a very nicely readable yet concise and carefully balanced text. ... It contains a lot of motivation, detailed explanations, necessary pieces of underlying theory, references to useful book-length treatments of various topics, and examples of the code illustrating how to implement concrete models in the BUGS language efficiently. ... this book also has a substantial pedagogical value. By reading this book carefully, redoing the examples, and thinking about them, one can learn a lot not only about BUGS, but also about Bayesian methods and statistics in general. ... highly recommended to a wide audience, from students of statistics [to] practicing statisticians to researchers from various fields." -ISCB News, 57, June 2014 "... truly demonstrates the power and flexibility of the BUGS software and its broad range of applications, and that makes this book highly relevant not only for beginners but for advanced users as well. ... a notable addition to the growing range of introductory Bayesian textbooks that have been published within the last decade. It is unique in its focus on explicating state-of-the-art computational Bayesian strategies in the WinBUGS software. Thus, practitioners may use it as an excellent, didactically enhanced BUGS manual that, unlike ordinary software manuals, presents detailed explanations of the underlying models with references to relevant literature [and] worked examples, including excerpts of WinBUGS code, as well as graphical illustrations of results and critical discussions. No doubt, The BUGS Book will become a classic Bayesian textbook and provide invaluable guidance to practicing statisticians, academics, and students alike." -Renate Meyer, Journal of Biopharmaceutical Statistics, 2014 "In this book the developers of BUGS reveal the power of the BUGS software and how it can be used in Bayesian statistical modeling and inference. Many people will find it very useful for self-learning or as a supplement for a Bayesian inference course." -William M. Bolstad, Australian & New Zealand Journal of Statistics, 2013 "If a book has ever been so much desired in the world of statistics, it is for sure this one. ... the tens of thousands of users of WinBUGS are indebted to the leading team of the BUGS project for having eventually succeeded in finalizing the writing of this book and for making sure that the long-held expectations are not dashed. ... it reflects very well the aims and spirit of the BUGS project and is meant to be a manual 'for anyone who would like to apply Bayesian methods to real-world problems.' ... strikes the right distance between advanced theory and pure practice. I especially like the numerous examples given in the successive chapters which always help readers to figure out what is going on and give them new ideas to improve their BUGS skills. ... The BUGS Book is not only a major textbook on a topical subject, but it is also a mandatory one for all statisticians willing to learn and analyze data with Bayesian statistics at any level. It will be the companion and reference book for all users (beginners or advanced) of the BUGS software. I have no doubt it will meet the same success as BUGS and become very soon a classic in the literature of computational Bayesian statistics." -Jean-Louis Fouley, CHANCE, 2013 "... a two-in-one product that provides the reader with both a BUGS manual and a Bayesian analysis textbook, a combination that will likely appeal to many potential readers. ... The strength of The BUGS Book is its rich collection of ambitiously constructed and thematically arranged examples, which often come with snippets of code and printouts, as well as illustrative plots and diagrams. ... great value to many readers seeking to familiarize themselves with BUGS and its capabilities." -Joakim Ekstrom, Journal of Statistical Software, January 2013 "MCMC freed Bayes from the shackles of conjugate priors and the curse of dimensionality; BUGS then brought MCMC-Bayes to the masses, yielding an astonishing explosion in the number, quality, and complexity of Bayesian inference over a vast array of application areas, from finance to medicine to data mining. The most anticipated applied Bayesian text of the last 20 years, The BUGS Book is like a wonderful album by an established rock supergroup: the pressure to deliver a high-quality product was enormous, but the authors have created a masterpiece well worth the wait. The book offers the perfect mix of basic probability calculus, Bayes and MCMC basics, an incredibly broad array of useful statistical models, and a BUGS tutorial and user manual complete with all the 'tricks' one would expect from the team that invented the language. BUGS is the dominant Bayesian software package of the post-MCMC era, and this book ensures it will remain so for years to come by providing accessible yet comprehensive instruction in its proper use. A must-own for any working applied statistical modeler." -Bradley P. Carlin, Professor and Head of Division of Biostatistics, University of Minnesota, Minneapolis, USA

Synopsis

In recent years, Bayesian methods have become the most widely used statistical methods for data analysis and modeling. The BUGS software has become the most popular software for Bayesian analysis worldwide. Authored by the team that originally developed this software, "Bayesian Analysis using BUGS" provides a practical introduction to this program and its use. The text presents complete coverage of all the functionalities of BUGS, including prediction, missing data, model criticism, and prior sensitivity. It also features a large number of worked examples, a wide range of applications from various disciplines, and numerous detailed exercises in every chapter.

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Amazon.com: 7 Rezensionen
12 von 12 Kunden fanden die folgende Rezension hilfreich
A really useful book. 5. Dezember 2012
Von Rasmus Bååth - Veröffentlicht auf Amazon.com
Format: Taschenbuch
There are many books on Bayesian statistics but many, I found, are heavy on theory but slim on practicalities. The BUGS book is the opposite, it is a practical guide to doing Bayesian statistics full of recipes of how to actually analyze lots of different types of data. A truly useful and well written book.
8 von 8 Kunden fanden die folgende Rezension hilfreich
Mixed feelings 13. Januar 2013
Von Dimitri Shvorob - Veröffentlicht auf Amazon.com
Format: Taschenbuch
The authors deserve huge credit for developing BUGS and supporting documentation (OpenBUGS has not one but two relevant menu items, "Manuals" and "Examples", and "Examples" have three "core" volumes, plus a few specialized ones), and their contribution has been duly recognized, so here I want to put piety aside, focus on the book, and express appreciation (a definite "buy") mixed with disappointment.

There are two complaints of "the book is not what I expected" nature. "The BUGS Book" is not an engaging, accessible and, well, nice-to-look-at book like "Bayesian modeling using WinBUGS" by Ntzoufras ($80), but a serious, LaTeX-typeset affair, and this is a missed opportunity to popularize Bayesian methods. At the same time, "The BUGS Book" does not talk enough about ... BUGS. Discussion of syntax is unfortunately split between "inline text" and appendices at the end of the book, and is materially incomplete compared to the doc; OpenBUGS menu items are barely discussed; R users will be disappointed. Far from being "the BUGS bible", "The BUGS Book" aspires to be a book on Bayesian methods, with BUGS illustrations, and, to a first approximation, builds on a selection of BUGS "Examples" by providing the "theory" context.

I do not penalize the book for not conforming to my preferences, but do complain when I find the writing confusing. It is difficult to give examples without extensive quotes, but if you have the book, let me point you to Example 3.4.1 on page 48, and ask

(a) Do you think that the "model" lines are ordered in the most intuitive way and the code is easy to follow? A trivial-sounding, but real and constant annoyance. I ended up re-ordering the lines in downloaded code - yes, the book has a web site, look closely at page xiv; there are also two BUGS mailing lists, but these are not in the book - to follow the logic.

(b) Why is coin.prob needed if we are after the posterior of theta, i.e. revised values of p? Actually, is there a posterior being derived? It seems like the p's are to be updated, but, syntax-wise, the thetas look on equal terms with the p's? Also, look at the thetas in Example 5.4.1 on page 95, and note the paragraph starting with "It is important to note..."

(c) Is the posterior available in closed form, or are we using MCMC? This one is a softball: the former, as MCMC methods are only introduced in Chapter 4, and BUGS-specific details are discussed ... in Chapter 12, on a total of two pages, pp. 298-299. This explains why the BUGS burn-in setting is not mentioned before Chapter 4 - it has to be somewhere on the two screenshots on page 19 - but, curiously, it does not seem to be mentioned after Chapter 4 as well.

Overall, a useful, unique book, but one that does not make it easy on the reader and which could be improved with just a little more effort from the authors. I am keeping my copy, but look forward to a second edition.
6 von 6 Kunden fanden die folgende Rezension hilfreich
Intro to Bayes/MCMC and enough on BUGS to get started 26. November 2012
Von Dan Wright - Veröffentlicht auf Amazon.com
Format: Taschenbuch Verifizierter Kauf
This is the long awaited BUGS book. First thing, it is not a manual (if you want the manual, that is a few thousand pages online and will cost more in printing and paper than this book). This is an introduction to Bayes and MCMC using BUGS notation (so not the graphs, which the authors note can be cumbersome). It provides enough info to get started so should be fine for an intro Bayes course (prereqs intro calc and probably regression 1, there are enough equations that and reading BUGS code may put off non-mathematical folks).

The book is shorter than what I was expecting. It does not go into as much detail about why certain priors should be used as some other sources, but it does talk about evaluating the sensitivity of this choice (or choices). I found these sections particularly useful. The examples are spread across the sciences, and presumably if instructors really want only their discipline surveyed they can add their own.

The authors provide details for accessing the different versions of BUGS including those accessible from R. Given that R handles data manipulation well, this will probably become the prefered method by most. There is also a section on WBDev for advanced users writing their own functions. The appendices should be useful when the reader is running BUGS.
5 von 5 Kunden fanden die folgende Rezension hilfreich
The Canonical Book on BUGS 4. Dezember 2012
Von John Myles White - Veröffentlicht auf Amazon.com
Format: Taschenbuch Verifizierter Kauf
I've been using the BUGS (in the form of JAGS) to do Bayesian modeling for several years now. Over those years, I've had to reverse engineer BUGS in order to get my work done. This reverse engineering process involved endless Google searches to locate the occasional web forum, journal paper or blog post that would directly address my problem.

None of that would have been necessary if I'd had "The BUGS Book" earlier: it consolidates all of the core information about BUGS in one location and directly addresses many of the most confusing issues that come up in practical use of BUGS. It will be the definitive reference for years to come.
5 von 5 Kunden fanden die folgende Rezension hilfreich
Misleading Title 30. Mai 2013
Von D. Collingridge - Veröffentlicht auf Amazon.com
Format: Taschenbuch Verifizierter Kauf
If you are someone looking for a good introduction on how to use Bugs in practical modeling applications, I am sorry to say that this is not your book. At first I was excited to see the types of scenarios that the authors present in this book. They are good real-world applications, but my excitement quickly dissipated when I realized that the authors do not provide enough information on their codes. In other words they don't explain why they used certain codes; in most cases they don't even identify the purpose of lines in their codes with a # sign, a common practice recommended by other Bugs authors. For example, the non-linear regression starting on page 109 presents the code without # tag explanations, presents the output without adequate descriptions, and gives barely a *half* page of text explaining the rationale behind the code. I am at an intermediate level with Bugs and struggled with most of the examples. This book is probably ideal for more advanced users which is why I will keep it. It should prove useful when my knowledge catches up. Until then, back to more basic books.
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