If you're really interested in the Banach-Tarski Paradox this book is what you have to read. The straightness of thoughts of Stan Wagon and his beautiful ideas not only about the paradox but also set theorie in general were so impressing, that thi s book became one of my favourites.

I recommend this textbook to anyone studying, teaching, or merely involved in the use of statistics. It is comprehensive and insightful yet easy to interpret and apply. Hu and Toh must be commended on their knowledge of the field and for a job very well done.

This book treats some stuffs in geostatistics including basic stochastic theory, variogram, and various kriging techniques. Though this is an introductory textbook of geostatistics, it's theoretical approach and explanation are clear, creative, and easy to understanding for students who are new to this area. I would like to recommend this book for all of students who have an interest in geostatistics and for experts too.

For those of you who wish to have a basic understanding of statistics, I recommend Basic Statistics by Hopkins, Hopkins, & Glass. This textbook will take the most novice statistician from the mean, mode, and median into two-factor anova with a sense of humor. It contains several understanding checks and answers in each chapter, enabling the reader to stop and review difficult information. Each chapter also contains active reviews, mastery tests, and answers for concept. These authors have made this text easy to read and easy to understand, which is a difficult task for such difficult mathematical information.

Tales of Distribution is fun, well written and easy to read. Tales of Distribution is a college textbook worth reading if you need to put statistics at your command! Spats'z style is helpful to the reader and down to earth. This is a heavy-duty treatment of the topic; however, interesting examples of how to use statistics in day-to-day life makes this topic come alive. If you read this book, you will never be intimidated by statistics in your lifetime.

The author's discussion of "Rationale of ANOVA" and "Hypothesis Testing" is excellent. Frequent use of algebraic explanation is joined with graphic explanations to help the reader visualize the author's points. "Work Problems" at the end of each chapter show imagination and creativity. The author seems to have a sincere desire to help the reader at every turn along their journey of statistical discovery. The author also seems to have privilege insight into the reader's mind. He anticipates what the reader is thinking, and serves up a helping of practical advice at the exact perfect moment.

As a market-research practitioner, I found the book to be very refreshing and enjoyable to read. I read it on my summer vacation! My daughter purchased the book as the required text for her statistics class at UCLA. At the end of her class she had planned to sell the book back to the UCLA bookstore. After looking at the book for 60 seconds, I said to her "No, don't sell this book. This is great book. Keep it." To which she replied; "But Dad, I need the money. You can keep it if you pay me for it." To which I replied; "Haven't I paid for this book once already? If I pay you for it now, that would be the second time I bought the same book."

This book features a deep and focused lesson on Bayes and Empirical Bayes Methods. It goes through the key topics as conjugate priors, MCMC methods (non iteratives and iteratives as the well known Gibbs samplining and metropolitis hastings algorithms), model selection methods (as bayes factor) and issues related as model robusteness.
The Approach is increasingly formal and deeply complex, allowing for getting the basics or diving into more complex knowledge according to your former background. You need at least a good understanding of Frequentist statistic to be able to follow the reasonings. Each chapter allow you to stop at some point without losing the thread. Last part of the book is in fact deep knowledge demanding.
The most interesting point of this book according to my very limited statistics background is that it makes good comparations with the frequentist approach (classical approaches as confidence intervals and point estimators), checking performance of either method. Even, it features some combination of both approaches getting some bayessian intervals.
As a negative point, I would say that examples are hard to follow for someone with limited bakground and too much complex. They really do not clear me up enough.
All in all, is a very profitable book for jumping into bayesian methods.

Robert is the author or co-author of a number of excellently written statistical texts from a Bayesian viewpoint. This text is no exception. It was quite popular in its first edition in 1994 (a translation and correction of an earlier text in French). The rapid advancement in Bayesian applications and theory due to the success of computer-intensive methods such as Markov Chain Monte Carlo Methods justifies an update in 2001.

Chapter 7 on model choice is entirely new and Chapter 6 on Bayesian calculations is extensively revised. Chapter 10 on hierarchical models and empirical Bayes extensions has been supplemented with a number of recent examples. Bayesian hierarchical models are now being used in the development of clinical trials particularly in the medical device industry.

This is an advanced graduate text in Bayesian statistics and has a wealth of references to the literature. In that respect it is very similar to the fine text by Bernardo and Smith (1994) "Bayesian Theory" but is a little more current.

An important reference for all statistical researchers, I highly recommend it for a graduate course text in Bayesian methods as well as for a reference book.

As a reader with an economical background, mathematical texts are usually hard to be followed. Nevertheless, dinamic models through bayesian forecasting are afordable with this book. Introductory chapters on the bayesian learning algorithm and univariate models rough out the kernel of the issue. Once you dive into the following more complicated chapters you can get lost but the main idea is got. To avoid getting lost, several readings are necessary. Finally, last chapters for non linear models, models with exponential distributions and MCMC methods are really heavy going but a light reading can allow you to get a general overview.

All in all, is a great workbook. The main drawback may be the lack of more practical examples to illustrate the theoretical concepts.

This book is a landmark in both neural networks and in statistics. It describes a coherent and powerful framework for using supervised neural networks, and it contains radical ways of making Bayesian Monte Carlo computations in high-dimensional spaces more efficient.

the sampling vs population table for the 99% confident interva