Book Description
There is a strong upsurge in the use of Bayesian methods in applied statistical analysis, yet most introductory statistics texts only present frequentist methods. In Bayesian statistics the rules of probability are used to make inferences about the parameter. Prior information about the parameter and sample information from the data are combined using Bayes theorem. Bayesian statistics has many important advantages that students should learn about if they are going into fields where statistics will be used. This book uniquely covers the topics usually found in a typical introductory statistics book but from a Bayesian perspective.
Customer Reviews:
A Great Foundation for Learning Bayesian Statistics.......2007-08-23
For people who need a primer in Statistics, especially Bayesian Statistics, I'd recommend this book.
When you are finished with this book, you can apply basic Bayesian methods for common case scenarios involving Normal distributions and Binomial distributions. Pragmatic scenarios for understanding how to interpret the results and understand when your prior may be inappropriate for your data were quite welcome and missing or underrepresented in other books and seminars I've taken in Bayesian statistics.
I especially appreciated the discussions on how to perform hypothesis tests in a Bayesian framework that is rare to find.
Bolstad does an excellent job in showing the relationship between Bayesian and Frequentist methods but, in my opinion, doesn't do enough exclamation points in the cases where he shows that they mathematically converge.
What is especially GOOD about this book is the fact that it DOESN'T get into the heavy mathematical underpinnings, history, and rationale for Frequentist vs Bayesian approaches. I have taken several seminars and read several books which focus on this approach of introducing rigorous mathematical formalism and integration using Markov Chain Monte Carlo (MCMC) was left somewhat bewildered by it. The PhD's who worked with me and tried to explain why Bayesian methods were better utterly failed because they overemphasized the high-falutin' mathematical rigor In fact, I would go so far as to say that the university professors and statisticians who emphasize these techniques are actually holding back the advancement and use of Bayesian methods by the general practicioners because of this. The average person doing statistics is going to do it by rote (whether we like it or not) so providing accessible methods to people who can do this is the way to further the cause.
Bolstad's book is a great foundation because it doesn't try to be comprehensive and mathematically rigorous book. It focuses on providing just enough mathematical underpinnings to understand the basic concepts and make progress without dwelling on it.
It would be great if Bolstad's book were used in high schools or in freshman college and university courses to introduce statistics. We'd convert more people to Bayesian methods if we did.
Introduction to Bayesian Statistics not so good...........2007-01-09
Not enough formal. Subjects exposition is not accurate. It would make a good high schoold book but doesn't meet formalism requirements for academical purposes.
Intro to Bayesian Bolstad.......2006-12-25
This is an outstanding introduction for anyone with a modest knowledge of algebra. It includes some of the clearest expositions of fundamental statistical concepts and then extends and re-interprets those concepts in a way that makes Bayesian logic natural and intuitive. Excellent problems to solve illustrating and solidifying the concepts of each chapter. First rate reading!
I learned a great deal about how the Bayesians do statistics.......2004-05-25
Approximately ten years ago, I received my initial statistics instruction from Dr. Robert Hogg, one of the leading educators in the field. There were occasions in class when he referred to the Bayesians, calling them a group of statisticians who rely on separate "a priori" and "a posteriori" analyses. As was his style, he made several jokes about "a posteriori" data. The structure of the class was such that he could not spend a great deal of time on Bayesian statistics, but his brief comments have always remained in my mind.
Therefore, when I received this book I immediately decided that I would read it. From it, I learned that the Bayesian approach to statistics is valuable and more accurately reflects the way humans think about the world. There are two primary philosophical approaches to statistics, the frequentist and Bayesian, with the frequentist being that most widely covered in basic statistics classes. A frequentist statistician uses random samples to provide estimates for unknown parameters of populations.
The Bayesian approach considers the population parameters to be random variables. The process of determining the value of a parameter starts with a subjective prior distribution of the parameter before the data is analyzed. After the data is collected and organized, Bayes' theorem is then used to revise your beliefs about the values of the parameters.
The first sections deal with the basics of summarizing and displaying data; logic, probability and uncertainty. These sections are generally not different from frequentist statistics, so there is no distinction between the Bayesian and frequentist philosophies. The first real differences occur at the end of chapter 5, which covers logic, probability and uncertainty. This is the point where Bayes' theorem is introduced and the principles of prior and posterior probabilities. Chapter 5 describes discrete random variables, and again, this section is standard material on probability.
The true philosophy of Bayesian statistics appears in chapter 6, which covers Bayesian inference for discrete random variables. As a newcomer to this area, I read it with great interest and learned a great deal about how Bayesian operations are performed. The remaining sections deal with the processes of performing basic statistical operations using Bayesian methods. This includes:
* Bayesian inference for binomial proportion.
* Bayesian inference for normal mean.
* Bayesian inference for difference between means.
* Bayesian inference for simple linear regression.
There are also two chapters that compare the Bayesian and frequentist techniques. Chapter 9 compares the Bayesian and fequentist techniques for the inference for proportions and chapter 11 compares the techniques for the inference for means. Exercises are included at the end of each chapter and appendix F is devoted to the answers to odd-numbered exercises.
I learned an enormous amount about Bayesian methods from this book and I strongly recommend it if you are interested in learned how the Bayesians do things.
Published in the recreational mathematics e-mail newsletter, reprinted with permission.
Book Description
This book is a contemporary introduction to theory, methods and computation in Bayesian Analysis. It focuses on topics that have stood the test of time and emerging areas such as reference priors, objective Bayes testing, Bayesian model selection and wavelets. No other such book is available in the market.
Book Description
The Bayesian revolution in statistics - where statistics is integrated with decision making in areas such as management, public policy, engineering, and clinical medicine - is here to stay. Introduction to Statistical Decision Theory states the case and in a self-contained, comprehensive way shows how the approach is operational and relevant for real-world decision making under uncertainty.
Starting with an extensive account of the foundations of decision theory, the authors develop the intertwining concepts of subjective probability and utility. They then systematically and comprehensively examine the Bernoulli, Poisson, and Normal (univariate and multivariate) data generating processes. For each process they consider how prior judgments about the uncertain parameters of the process are modified given the results of statistical sampling, and they investigate typical decision problems in which the main sources of uncertainty are the population parameters. They also discuss the value of sampling information and optimal sample sizes given sampling costs and the economics of the terminal decision problems.
Unlike most introductory texts in statistics, Introduction to Statistical Decision Theory integrates statistical inference with decision making and discusses real-world actions involving economic payoffs and risks. After developing the rationale and demonstrating the power and relevance of the subjective, decision approach, the text also examines and critiques the limitations of the objective, classical approach.
Book Description
An essential introductory text linking traditional biostatistics with bayesian methods
In recent years, Bayesian methods have seen an explosion of interest, with applications in fields including biochemistry, ecology, medicine, oncology, pharmacology, and public health. As an interpretive system integrating data with observation, the Bayesian approach provides a nuanced yet mathematically rigorous means of conceptualizing biomedical statistics--from diagnostic tests to DNA evidence.
Biostatistics: A Bayesian Introduction offers a pioneering approach by presenting the foundations of biostatistics through the Bayesian lens. Using easily understood, classic Dutch Book thought experiments to derive subjective probability from a simple principle of rationality, the book connects statistical science with scientific reasoning. The author shows how to compute, interpret, and report Bayesian statistical analyses in practice, and illustrates how to reinterpret traditional statistical reporting--such as confidence intervals, margins of error, and one-sided p-values--in Bayesian terms. Topics covered include:
* Probability and subjective probability
* Distributions and descriptive statistics
* Continuous probability distributions
* Comparing rates and means
* Linear models and statistical adjustment
* Logistic regression and adjusted odds ratios
* Survival analysis
* Hierarchical models and meta-analysis
* Decision theory and sample size determination
The book includes extensive problem sets and references in each chapter, as well as complete instructions on computer analysis with the versatile SAS and WinBUGS software packages as well as the Excel spreadsheet program. For professionals and students, Biostatistics: A Bayesian Introduction offers an unique, real-world entry point into a remarkable alternative method of interpreting statistical data.
Book Description
The 2nd Edition of Dr. Winkler's classic book, first published in 1972, includes a CD Rom, Perspectives, and updated material. However, the basic concepts of Bayesian inference and decision have not really changed. This book gives a foundation in the concepts, enables readers to understand the results of Bayesian inference and decision, provides tools to model real-world problems and carry out basic analyses, and prepares readers for further exploration.
Customer Reviews:
Bayesian Inference and Decision.......2005-08-21
This is definitely useful for someone going for an MBA or other managerial degree. I would not have been able to do my class assignment without it. I am taking a graduate course in Statistics and this reference may help me achieve an A.
Get Past The Title... This Is A Key Reference Book.......2003-07-22
Not only is this a top notch instruction tool from a well-respected author, but for anyone whose work or interests lay in the risk and decision analysis field, this is a "must have" reference book. It may seem oxymoronic to say that a book on statistical inference, "probability", or Bayesian methods is interesting, but that is exactly what the author has achieved. Winkler makes what many of us may view to be a complex and intimidating topic understandable and enables practical implementation of the concepts. More than just a simple re-printing, the second edition provides updated references, additional readings and assessments of developments in the chapter scope since the first edition.
The initial chapter in the book provides an introduction to probabilistic thought and Bayes' theorem. Following chapters deal with discrete and continuous distributions, decision theory (practical applications...including influencing factors such as utility and subjective probability), Value of Information, and Bayesian approaches to hypothesis testing (quite meaningful for six-sigma thinkers, efficient resource appraisal decisions, and other business applications).
Practitioners will have to get past the rather academic title. If you model or work with uncertainty distributions, dependency, correlation, or contingent portfolio analysis, this book will provide the necessary conceptual understanding that will save you time and allow you to provide a better, more valid product.
The CD that accompanies the book provides standard reference tables and work-throughs for many of the problems/examples. All figures found in the book are included as are all chapter exercises (MS Word format). It also provides trial versions of decision software such as Lumina's Analytica (object based simulation) and TreeAge's Data (decision trees). A series of distribution generators are included as a plus for those of us who have a simulation modeling bent.
There is folklore that is tied this title. So well received was the first edition that rumors persist regarding people resorting to beaten up photocopies of the book. You don't have to do that anymore.
Book Description
About two hundred and forty years ago, an English clergyman named Thomas Bayes developed a method to calculate the chances of uncertain events in the light of accumulating evidence. Though his method has extensive applications to the work of economists, it is only recent advances in computing that have made it possible to exploit its full power.In this new and expanding area, Tony Lancaster 's text provides a comprehensive introduction to the Bayesian way of doing applied economics. Using clear explanations and practical illustrations and problems, the text presents innovative, computer-intensive ways for applied economists to use the Bayesian method. In addition, each chapter includes numerical and graphical examples and demonstrates their solutions using the S programming language and Bugs software.
Customer Reviews:
Good book!.......2007-04-12
I'm taking an intro to Bayesian stats class and we are using the Carlin & Louis book -- it is a POS.
I found this book in the library and it is much better. It gives very clear explanations of the ideas, and lots of concrete worked examples. Props to the author.
Clear as crystal and plenty of workouts.......2006-07-25
This is as clear as crystal. It also has plenty of workouts. It is a very useful volume.
Great Book.......2006-03-19
If you are looking for a book for Bayesian estimation, this is the one. The first several chapters say it all about Bayesian methods. The rest of the book is applications. Very helpful reading for learning the method.
Book Description
Among statisticians the Bayesian approach continues to gain adherents and this new edition of Peter Lee's classic introduction maintains the clarity of exposition and use of examples for which the text is known and praised. In addition, there is extended coverage of the Metropolis-Hastings algorithm as well as an introduction to the use of BUGS, as this is now the standard computational tool for such numerical work. Other alterations include new material on generalized linear modeling and Bernardo's theory of reference points.
Customer Reviews:
a review.......2006-09-17
This book has a clean selection of materials as an introduction to bayesian statistics. It is quite readable. Two problems however: 1) the formula derivation and reasoning often have intermediate steps skipped. You need to think for a while for derivations and his texts. In particular, you need to figure out by yourself which theorem or previous results that the derivation is based on. 2) typos. the 3rd printing still has typos not listed in the author's page, not too many but not trivial either.
Anyway, I still recommand this book because no better introductory bayesian book found yet.
good intermediate text.......2002-09-10
Although only the second edition is listed, I have read only the first 1989 edition and my review is for that edition. Lee wrote this book with the goal of teaching an introductory course in Bayesian statistics to his students at York University. He wanted a text that was more mathematical and deatiled than Lindley (1965) but not quite at the level of Box and Tiao.
This text achieves that goal. It was published at the time when MCMC methods were only starting to be appreciated. So the wider use of general prior distributions and hierarchical models does not yet enter into this book. I would assume that the second edition published in 1997 was written to remedy this shortcoming but I have not seen if it does.
Good introduction to basic theory of Bayesian statistics.......2001-12-05
This is a simple and easy-to-read introduction to the basics of Bayesian statistics, for someone with some previous exposure to statistical methods and theory. Lee does not try to do too much with this book. It's not too taxing on the brain, uses simple and easy-to-follow notation, and has a helpful appendix of common statistical distributions. I like the emphasis on conjugate priors, which are the mathematically most tractable Bayesian models that are often not treated fully in other texts. (Someone still needs to write the definitive text on conjugate Bayesian models.)
The book is limited in scope, a strength if you're just getting started on this topic, but will frustrate once you get into this stuff. There are plenty of other good books that go beyond the basics once you're ready.
Customer Reviews:
A surprising book.......2000-07-24
I have to admit, this wasn't a book I thought I'd read, but I came across it in a college library, glanced through it and ultimately later decided to buy and read it. I was surprised at the odd ways that decisions are made by everyday people, and the misconceptions upon which those decisions are made. Once explained, the reality of some premises was quite clear, and I could even see where I myself had been led astray in my own decision making on more than one occasion. This is a clearly written work that even the average reader will understand and appreciate.
Average customer rating:
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Bayesian Statistics: An Introduction (Arnold Publication)
ASIN: 9812383565 |
Book Description
This book provides a multi-level introduction to Bayesian reasoning (as opposed to "conventional statistics") and its applications to data analysis. The basic ideas of this "new" approach to the quantification of uncertainty are presented using examples from research and everyday life. Applications covered include: parametric inference; combination of results; treatment of uncertainty due to systematic errors and background; comparison of hypotheses; unfolding of experimental distributions; upper/lower bounds in frontier-type measurements. Approximate methods for routine use are derived and are shown often to coincide — under well-defined assumptions! — with "standard" methods, which can therefore be seen as special cases of the more general Bayesian methods. In dealing with uncertainty in measurements, modern metrological ideas are utilized, including the ISO classification of uncertainty into type A and type B. These are shown to fit well into the Bayesian framework.
Book Description
Pollard's introduction to the logic and techniques of Bayesian analysis is aimed at evaluation researchers. Although there is increasing interest in the approach among evaluators, most are unaware of what it has to offer. He addresses basic questions such as: What is it? How can it be applied? How is it different from other approaches? What advantages does it offer? Readers are assumed to have familiarity with programme evaluation and a sound knowledge of statistics.
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