Book Description
Incorporating new and updated information, this second edition of THE bestselling text in Bayesian data analysis continues to emphasize practice over theory, describing how to conceptualize, perform, and critique statistical analyses from a Bayesian perspective. Its world-class authors provide guidance on all aspects of Bayesian data analysis and include examples of real statistical analyses, based on their own research, that demonstrate how to solve complicated problems. Changes in the new edition include: · Stronger focus on MCMC · Revision of the computational advice in Part III · New chapters on nonlinear models and decision analysis · Several additional applied examples from the authors' recent research · Additional chapters on current models for Bayesian data analysis such as nonlinear models, generalized linear mixed models, and more · Reorganization of chapters 6 and 7 on model checking and data collection Bayesian computation is currently at a stage where there are many reasonable ways to compute any given posterior distribution. However, the best approach is not always clear ahead of time. Reflecting this, the new edition offers a more pluralistic presentation, giving advice on performing computations from many perspectives while making clear the importance of being aware that there are different ways to implement any given iterative simulation computation. The new approach, additional examples, and updated information make Bayesian Data Analysis an excellent introductory text and a reference that working scientists will use throughout their professional life.
Customer Reviews:
Comprehensive, but not well-written.......2007-01-06
This book is a very comprehensive treatment of Bayesian data analysis. However, it is not well-written. I find Lancaster's book to be much more well-written and interesting to read.
Very Excellent, but non-statisticians should start elsewhere.......2006-06-05
Gelman's book is an excellent and complete introduction to Bayesian methods. It covers a number of topics not touched by other intros I've read, and focuses much more on regression and ANOVA than other texts.
There are two downsides, coming from someone in psychology. First, the book seems to hover between an introductory text and a more advanced one. The topics covered are mostly introductory, but the examples aren't always entirely easy to follow. A tighter integration with the R and Bugs code would help. Perhaps a section at the end of the chapters containing a code example for each topic would be ideal. It's not that the topics themselves are necessarily opaque, but Gelman moves too fast at times, making it hard to think in terms of notation, theory, experimental design AND code at the same time (for those of us constantly thinking about how this affects our own research).
Second, as a general rule, this book is outside the ken of most psychologists. This is unfortunate since the methods are ideal for our discipline, and since many psychologists already perceive a large barrier of entry to statistics. As a psychologist with minimal undergraduate training in stats, I would (and did) start with a standard statistics book like Casella and Berger, and then move on to a gentler introduction to Bayesian methodology, like _Bayesian Methods: A Social and Behavioral Sciences Approach_ by Jeff Gill. Also, you can barely do anything in this book with SPSS so you'll have to learn R and Bugs.
As Good As It Gets For An Intro To Bayes.......2005-10-28
Yes, it is an introduction to Bayesian methods. That means you have to have a very good understanding of classical statistics (at the level of Casella and Berger would be optimal) and then be willing to use the WinBugs program to further your knowledge. A great book.
It is a good book, but not a bible of Bayesian analysis........2005-08-31
[1] A good introductory book, but definitely not a bible of Bayesian analysis.
[2] The example-based introduction may be a try of new generation of Bayesian. Many people, especially the beginners, may like this style.
[3] Some of the authors are good at programming in BUGS, R, etc, so the part of MCMC methods seems worthy to skim through.
[4] The book is suitable for the undergraduate and the first year graduate level.
A good introductory book, but..........2005-01-26
I read the other reviews and agree with them to some extent. This is
a good introduction to applied Bayesian analysis. Lots of
good examples, illustrations and exercises.
If you are the kind of person who learns by way of examples, then
this might be the text book for you. If you are looking for the
bigger picture, then you will be lost here. There is very little in the way
of theory. Why is this the right method? What is gained theoretically
over a frequentist method? What are the theoretical properties of the
proposed approach? To a large extent these kinds of questions remain a mystery.
In terms of flexibility an applied Bayesian approach has some decided
advantages. However, in terms of theory
it's almost as if the authors want you to believe that once
you adopt the Bayesian approach then the benefits of averaging
by way of using a prior will always be the right thing to do.
You could argue that advanced questions like this are better suited for
a more advanced text book. I tend to ask more out of a book.
Book Description
Now you can apply the techniques that business analysts at leading companies use to analyze and transform data into bottom line results. For more than 10 years, well-known consultant and business professor Wayne Winston has been teaching corporate clients and MBA candidates the most effective ways to use Microsoft Excel for data analysis, modeling, and decision making. This practical, business-focused guide delivers the best of Winston's classroom experience to you in 70+ concise chapters, organized by real-world scenarios. Quickly find and apply exactly the information you need to solve a specific business problem#151;from asset allocation modeling to estimating exponential growth, forecasting sales, optimizing portfolios, and other critical functions. You also get all the book's sample files on CD-ROM#151;ready for use in your own work.
Customer Reviews:
Great Book.......2007-07-21
Excellent...It really help me to better understand the data analysis with many differents case scenarios...exercises...its for everyone.
Real Good for a textbook........2007-05-12
I had to use for a college class, but great speed in shipping.
Not bad, but not as good as expected.......2007-04-13
I am an intermediate to advanced Excel user, so my review may reflect that level. As others have said, it looks like MS rushed this book to the market, evidenced by so many errata, which can be disappointing especially when the solution is wrong. On the other hand, there are some very interesting and genuine uses of various Excel functions to solve business problems.
I wouldn't recommend this book for beginners. If you're trying to learn Excel, this is not the book. It is not a book to teach excel, but a book to teach you what you can do with Excel to solve everyday problems, given you're familiar with the mechanics of excel.
I would recommend it with these caveats. And getting Walkenbach's book on Excel functions along with this would be very helpful in my opinion. Best of luck in your endeavors.
Excellent.......2007-04-11
I have read many books in excel, but this book is really the most beneficial and excellent book I've used in my life. It is full with practical not theoritical examples. and you can benefit form it in your work.
Very practical, but full of errors.......2007-04-02
Overall, I like this book, even though it is somewhat confusing, both in scope and in the target audience.
The techniques of "naming the range" or writing the "if" formula are certainly targeted for beginners, but most of statistical tools are normally used by more advanced users.
The worst thing, though, is that the book is full of errors, both typos and mistakes in problem solutions on the disk. I consider myself an intermediate user, so finding an error in "instructor solution" was more like an additional challenge for me, but for the beginner this could be very frustrating.
On the positive side - I really liked the idea of problems in the end of each chapter; so many books just give you the theory and then you do not know how to solve a real life problem. For most of chapters, I knew the tools, but still had to spend time figuring out the best way to implement it for problem solving.
Very practical book, good for an intermediate users. Just be aware of the typos !
Book Description
"The outstanding strengths of the book are its topic coverage, references, exposition, examples and problem sets... This book is an excellent addition to any mathematical statistician's library." -Bulletin of the American Mathematical Society In this new edition the author has added substantial material on Bayesian analysis, including lengthy new sections on such important topics as empirical and hierarchical Bayes analysis, Bayesian calculation, Bayesian communication, and group decision making. With these changes, the book can be used as a self-contained introduction to Bayesian analysis. In addition, much of the decision-theoretic portion of the text was updated, including new sections covering such modern topics as minimax multivariate (Stein) estimation.
Customer Reviews:
Excellent book on Bayesian analysis.......2005-08-31
[1] It is a classical book written by an excellent mathematician, not a worker of statistics!
[2] Its mathematics is precise and fascinating.
[3] The philosophy of Bayesian statistics is well discussed.
[4] It's worthy to read it many times.
[5] At the time of its publication, the revolutionary computational statistics was still in gestation. So, it is unfair to criticize its lack of numerical simulation, etc. As a comlement, some pragmatistic books are recommended, such as J. Liu's book on MCMC methods, Tanner's Tools for Statistical Inference, etc.
Excellent book!!!.......2004-12-21
This book is awesome. This book is theoretical but it has enough examples to follow along. The author's presentation is clear in every step. The problems in the book are challenging but do-able. Don't need another reference book.
a very readable and useful book.......2001-06-15
The professor used this book in the math stat course I took 2 years ago. I did not like the book at first, it looks too long to be covered. but it turned to be very easy to follow (You still need to think, but the author, being a hero in this field, made a very clear presentation to the audience). As long as you invest some time and brain, you can get a lot from this book. The problems are very good and instructional too.
Book Description
Computer Manual to Accompany Pattern Classification and its associated MATLAB software is an excellent companion to Duda: Pattern Classfication, 2nd ed, (DH&S). The code contains all algorithms described in Duda as well as supporting algorithms for data generation and visualization. The Manual uses the same terminology as the DH&S text and contains step-by-step worked examples, including many of the examples and figures in the textbook.
The Manual is accompanied by software that is available electronically. The software contains all algorithms in DH&S, indexed to the textbook, and uses symbols and notation as close as possible to the textbook. The code is self-annotating so the user can easily navigate, understand and modify the code.
Customer Reviews:
Underwhelmed.......2007-04-04
Talk about over-hype from reviewer #1!
This "manual" is thin on substantive content, with TONS of whitespace & whitepages to stretch it out to ~125pages. The documentation of the code should be available as a PDF with files on MATLAB's file exchange or on the publisher's website. Save yourself some $$.
Excellent toolbox to learn & use........2004-07-09
I was one of the early access recipient of this toolbox and found it extremely useful. It basically has a whole bunch of cleaning and classification algos.
The toolbox also allows one to extend its use with new algorithms, tweaks or to use our dataset. As long as its formatted in the same fashion.
I would strongly recommend this toolbox, if you are looking for additional material, another book worth having is Christopher Bishop's book.
Book Description
Structural equation modeling (SEM) is a powerful multivariate method allowing the evaluation of a series of simultaneous hypotheses about the impacts of latent and manifest variables on other variables, taking measurement errors into account. As SEMs have grown in popularity in recent years, new models and statistical methods have been developed for more accurate analysis of more complex data. A Bayesian approach to SEMs allows the use of prior information resulting in improved parameter estimates, latent variable estimates, and statistics for model comparison, as well as offering more reliable results for smaller samples.
Structural Equation Modeling introduces the Bayesian approach to SEMs, including the selection of prior distributions and data augmentation, and offers an overview of the subject’s recent advances.
- Demonstrates how to utilize powerful statistical computing tools, including the Gibbs sampler, the Metropolis-Hasting algorithm, bridge sampling and path sampling to obtain the Bayesian results.
- Discusses the Bayes factor and Deviance Information Criterion (DIC) for model comparison.
- Includes coverage of complex models, including SEMs with ordered categorical variables, and dichotomous variables, nonlinear SEMs, two-level SEMs, multisample SEMs, mixtures of SEMs, SEMs with missing data, SEMs with variables from an exponential family of distributions, and some of their combinations.
- Illustrates the methodology through simulation studies and examples with real data from business management, education, psychology, public health and sociology.
- Demonstrates the application of the freely available software WinBUGS via a supplementary website featuring computer code and data sets.
Structural Equation Modeling: A Bayesian Approach is a multi-disciplinary text ideal for researchers and students in many areas, including: statistics, biostatistics, business, education, medicine, psychology, public health and social science.
Customer Reviews:
This is the book you want.......2007-06-07
Bayesian methods are moving into structural equation modeling. The most sophisticated approach to modeling interactions is Bayesian. People who want to be able to predict the values of observed variables need a Bayesian approach.
This book, with the code and datasets available from the publisher's website, will help you to estimate SE models using the Bayesian approach and the free WinBUGS software. Yes, it's a math-heavy book, but Sik-Yum Lee does a great job explaining this very different approach. Lee demonstrates Bayesian methods applied to basic models, interaction models, mixture models, multi-level models, and models with non-normal distributions. You really want to have this book, if you are a serious SEM user.
Structural Equation Modelling: A Bayesian Approach.......2007-05-07
I would rather not recommend this book to whom is looking for SEM. This book is more like Math oriented..so it is difficult to mention that it is good for students seeking for answeres from the business or sociological perspectives.
Book Description
The past decade has seen a dramatic increase in the use of Bayesian methods in marketing due, in part, to computational and modelling breakthroughs, making its implementation ideal for many marketing problems. Bayesian analyses can now be conducted over a wide range of marketing problems, from new product introduction to pricing, and with a wide variety of different data sources.
Bayesian Statistics and Marketing describes the basic advantages of the Bayesian approach, detailing the nature of the computational revolution. Examples contained include household and consumer panel data on product purchases and survey data, demand models based on micro-economic theory and random effect models used to pool data among respondents. The book also discusses the theory and practical use of MCMC methods.
Written by the leading experts in the field, this unique book:
- Presents a unified treatment of Bayesian methods in marketing, with common notation and algorithms for estimating the models.
- Provides a self-contained introduction to Bayesian methods.
- Includes case studies drawn from the authors’ recent research to illustrate how Bayesian methods can be extended to apply to many important marketing problems.
- Is accompanied by an R package, bayesm, which implements all of the models and methods in the book and includes many datasets. In addition the book’s website hosts datasets and R code for the case studies.
Bayesian Statistics and Marketing provides a platform for researchers in marketing to analyse their data with state-of-the-art methods and develop new models of consumer behaviour. It provides a unified reference for cutting-edge marketing researchers, as well as an invaluable guide to this growing area for both graduate students and professors, alike.
Customer Reviews:
Rossi book is a must!.......2006-04-13
Rossi, McCulloch, and Allenby is a must read for any applied
quantitative doctoral student in Marketing or Statistics. ?It provides
real applications, how to fit Bayesian models, and the challenges and
choices that the researcher faces. ?In summary, it is a book that
highlights and legitimizes the field of Bayesian Marketing as its own
and important discipline. ?We owe the authors a debt of gratitude.
Book Description
This book examines advanced Bayesian computational methods. It presents methods for sampling from posterior distributions and discusses how to compute posterior quantities of interest using Markov chain Monte Carlo (MCMC) samples. This book examines each of these issues in detail and heavily focuses on computing various posterior quantities of interest from a given MCMC sample. Several topics are addressed, including techniques for MCMC sampling, Monte Carlo methods for estimation of posterior quantities, improving simulation accuracy, marginal posterior density estimation, estimation of normalizing constants, constrained parameter problems, highest posterior density interval calculations, computation of posterior modes, and posterior computations for proportional hazards models and Dirichlet process models. The authors also discuss computions involving model comparisons, including both nested and non-nested models, marginal likelihood methods, ratios of normalizing constants, Bayes factors, the Savage-Dickey density ratio, Stochastic Search Variable Selection, Bayesian Model Averaging, the reverse jump algorithm, and model adequacy using predictive and latent residual approaches. The book presents an equal mixture of theory and applications involving real data. The book is intended as a graduate textbook or a reference book for a one semester course at the advanced masters or Ph.D. level. It would also serve as a useful reference book for applied or theoretical researchers as well as practitioners. Ming-Hui Chen is Associate Professor of Mathematical Sciences at Worcester Polytechnic Institute, Qu-Man Shao is Assistant Professor of Mathematics at the University of Oregon. Joseph G. Ibrahim is Associate Professor of Biostatistics at the Harvard School of Public Health and Dana-Farber Cancer Institute.
Customer Reviews:
not a good starting point.......2004-12-19
You need to be clear what you are looking for. If you have vaguely heard that MCMC (Monte Carlo Markov Chain) methods are a neat way to apply Bayesian ideas to practical problems, and you want to use them, then this is *not* the book for you. Go to the splendid Gilks et al, Markov Chain Monte Carlo in Practice. Also check out BUGS, which is free software, originally written by Gilks and co and improved by many others.
If you want a more general introduction to Bayesian methods, then Gelman et al, Bayesian Data Analysis is excellent.
If you are unclear about the controversies and want to know why the Bayesian approach is correct, and the others are flat wrong, then read Ed Jaynes book.
So what is this book for. Well, I think you have to be a specialist, interested in further development of the techniques, and in the maths. As a previous reviewer has commented (correctly), in that case you probably have easy access to the journal literature and need to think carefully what extra benefits this book gives you.
Same writer reviewed book 4 times!.......2004-12-19
I depend upon the Amazon reviews to help determine whether to purchase a book as most others do. When a reviewer posts four 5 star reviews of the book (out of 7 total) it biases the rating and makes one wonder whether if the reviewer has an agenda or is related to the authors. This may be a great book, but I have no confidence from the rating given here.
extensive book on MCMC.......2002-10-18
This is truly an oustanding book on MCMC methods for Bayesian
computation. The authors present a nice balance between technical
developments and applications. It covers several topics not covered by other MCMC books, such as HPD regions, model selection, and density estimation. This book is world class.
two great books.......2002-10-17
This is an outstanding book on MCMC methods. The book presents
novel and sophisticated methods for carrying out posterior
computations and summarizing posterior quantities of interest using novel MCMC techniques. The authors present a lot of their
groundbreaking work as well as summarizing the work of many others. The book presents a number of complex models used in real and interesting applications in the biomedical sciences. Two of the authors also have wirtten another outstanding book titled Bayesian Survival Analysis (Ibrahim et al., 2001), which presents modern methods for Bayesian survival analysis and provides a comprehensive and thorough treatment of the subject. The authors are to be congratulated on writing two very fine books. Both books get 5 stars from me.
two great books.......2002-10-15
This is a great book by the authors, covering a wide range of
topics in MCMC. The coverage of the material is deep and novel.
Two of the authors also have published another outstanding book
titled Bayesian Survival Analyis, by Ibrahim et al., which presents
cutting edge and novel methods in the analysis of survival data.
Both books get 5 stars from me. A splendid job by the authors
in writing two very fine books.
Book Description
Appropriate for a one-term introductory statistics course, this text introduces statistical concepts and methods from a predominantly Bayesian perspective. It covers standard topics taking the Bayesian view that subjectivity is inevitable in science and that different conclusions from the same study are normal and stresses the advantages of this approach in scientific inference. It presents statistics as a means of integrating data into the scientific process and stresses data analysis and experimental design ideas early.
Customer Reviews:
Excellent introduction........2001-05-03
This is a truly clear and thoughtful introduction to Bayesian statistics.Nothing is taken for granted as the author leads you through examples and concepts. This was my first introduction to Bayesian statistics, and Berry makes it seem so much more reasonable and closer to real research/real life than the artifice involved in other approaches.
elementary statistics presented with the Bayesian approach.......2001-03-02
This is an excellent introductory text designed for a first course in statistics. It covers all the topics that are typically in a first course. However, all other texts at this level take the frequentist approach to inference. A few may have sections that introduce Bayesian ideas but the Bayesian approach is a paradigm for statistical inference and as such the approach should be incorporated in all statistical topics. Berry shows that this can be done without the student having to know calculus. To understand Bayesian methods the student mainly has to know that posterior probability = likelihood x prior probability. Berry provides a good list of references for those who want to pursue more advanced topics.
This book is unique. It demonstrate that statistics can be taught from the Bayesian approach in the very beginnning. This is much like what Noether did when he wrote an introductory text in statistics taking a strict nonparametric approach.
The text is loaded with exercises and the exposition is very clear. There are many useful and entertaining diagrams. Many examples are taken from real medical problems. Medicine is an area in which Berry has done a great deal of consulting and his experience shows in his examples. This should be the text to turn to if you want an introduction to the subject. If you know the basics and want more advanced treatment go to the references mentioned in Berry's preface.
An excellent introduction.......2000-02-25
This book completely fulfills its goals, one of which is not to be a definitive reference book. It provides a friendly, entertaining introduction into statistics from a Bayesian perspective.
Introduction book.......2000-02-04
It is not too useful for people beyond college level. Not as a reference book.
Book Description
Game theory, the formalized study of strategy, began in the 1940s by asking how emotionless geniuses should play games, but ignored until recently how average people with emotions and limited foresight actually play games. This book marks the first substantial and authoritative effort to close this gap. Colin Camerer, one of the field's leading figures, uses psychological principles and hundreds of experiments to develop mathematical theories of reciprocity, limited strategizing, and learning, which help predict what real people and companies do in strategic situations. Unifying a wealth of information from ongoing studies in strategic behavior, he takes the experimental science of behavioral economics a major step forward. He does so in lucid, friendly prose.
Behavioral game theory has three ingredients that come clearly into focus in this book: mathematical theories of how moral obligation and vengeance affect the way people bargain and trust each other; a theory of how limits in the brain constrain the number of steps of "I think he thinks . . ." reasoning people naturally do; and a theory of how people learn from experience to make better strategic decisions. Strategic interactions that can be explained by behavioral game theory include bargaining, games of bluffing as in sports and poker, strikes, how conventions help coordinate a joint activity, price competition and patent races, and building up reputations for trustworthiness or ruthlessness in business or life.
While there are many books on standard game theory that address the way ideally rational actors operate, Behavioral Game Theory stands alone in blending experimental evidence and psychology in a mathematical theory of normal strategic behavior. It is must reading for anyone who seeks a more complete understanding of strategic thinking, from professional economists to scholars and students of economics, management studies, psychology, political science, anthropology, and biology.
Book Description
READ ALL ABOUT IT!
David Spiegelhalter has recently joined the ranks of Isaac Newton, Charles Darwin and Stephen Hawking by becoming a fellow of the Royal Society. Originating from the Medical Research Council’s biostatistics unit, David has played a leading role in the Bristol heart surgery and Harold Shipman inquiries.
Order a copy of this author’s comprehensive text TODAY!
The Bayesian approach involves synthesising data and judgement in order to reach conclusions about unknown quantities and make predictions. Bayesian methods have become increasingly popular in recent years, notably in medical research, and although there are a number of books on Bayesian analysis, few cover clinical trials and biostatistical applications in any detail. Bayesian Approaches to Clinical Trials and Health-Care Evaluation provides a valuable overview of this rapidly evolving field, including basic Bayesian ideas, prior distributions, clinical trials, observational studies, evidence synthesis and cost-effectiveness analysis.
Covers a broad array of essential topics, building from the basics to more advanced techniques.
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Illustrated throughout by detailed case studies and worked examples
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Includes exercises in all chapters
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Accessible to anyone with a basic knowledge of statistics
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Authors are at the forefront of research into Bayesian methods in medical research
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Accompanied by a Web site featuring data sets and worked examples using Excel and WinBUGS - the most widely used Bayesian modelling package
Bayesian Approaches to Clinical Trials and Health-Care Evaluation is suitable for students and researchers in medical statistics, statisticians in the pharmaceutical industry, and anyone involved in conducting clinical trials and assessment of health-care technology.
Download Description
The Bayesian approach involves collecting data from past experience in order to reach conclusions about future events. Bayesian methods have become increasingly popular in recent years, notably in medical research. There are a large number of books on Bayesian analysis, but very few that cover clinical trials and biostatistical applications in any capacity. There is no book available that is introductory in nature and covers such a broad array of essential topics. This book provides a valuable overview of this rapidly evolving field, not only for statisticians in the pharmaceutical industry, but also to anyone involved in conducting clinical trials and HTA work. Comprehensive coverage of Bayesian methods in medical research
General
| Science
| Subjects
| Books
Bayesian Statistics: An Introduction (Arnold Publication)
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Data Analysis Using Regression and Multilevel/Hierarchical Models
The Elements of Statistical Learning
Modern Applied Statistics with S
