A First Course in Bayesian Statistical Methods (Springer Texts in Statistics) 2009th Edition

I have read the first 11 chapters of this book so far and found it pretty good. Studying this book doesn't require advanced mathematical knowledge such as measure theory which makes it suitable for a wide range of people who want to know more about the Bayesian framework. However, familiarity with basic Matrix Algebra and probability is somewhat required. In my opinion, the biggest advantage of this book is that it provides the reader with a deep understanding of Bayesian procedure concepts rather than purely mathematical formulation. In addition, the R code for all the figures is provided on the author's webpage which is another advantage. Other than some minor typos, I cannot think of any noticeable drawbacks which you can also find a list of them on the book's webpage as well.

This is a great introductory book for someone wanting an understandable, practical overview of the subject.I am currently half-way through the book, but have so far been very pleased. The book has clear and useful examples scattered throughout the chapters which help illuminate the ideas and procedures (I am doing this self-paced, not as part of a course). The book also appears to cover a wide variety of topics; several of my coworkers are very familiar with Bayesian Statistics and looking through the table of contents they seemed interested in reading it after I was done.Overall, I find this a good intro book. It doesn't focus on proofs and theorems, but on using the mechanisms presented.

I am a first year student in a PhD program in statistics with near zero statistics background prior to this year. I must say, this is probably the best balance of theory/applications I've come across thus far. The proofs are rigorous but not untouchable to many non-statistics students in the course (ie, epidemiology and related fields requiring a statistics background). Book is thorough and the applications/figures well-motivate the topic area. Definitely a great book to read through and then keep on the shelf.Content wise, when I first got the book, naturally I paged to the end and legitimately the entire thing looked foreign/illegible to me more or less it seemed so advanced. However, PHoff presents the material in such a way that he provides just enough theory to make the results feel intuitive but not cumbersome, which really helps downstream with your ability to recall and actually APPLY the results within the book. The book basically takes you from zero bayesian background to understanding, coding, and applying fairly rigorous techniques for seemingly cumbersome statistical models that will feel intuitive to you by the end.Only drawback to the book is that there is no second edition. There are a number of errors throughout the book that definitely detract somewhat from the content. However, if you check PHoff's website, he has noted (as far as I can tell) all of them in annotations by page, which helps this issue a bit.Also, a "first course in bayesian statistics" as per the preface assumes you have a very solid understanding of fundamentals of probability and statistics (NOT that you know how to use a normal distribution, or other statistical results, you should be familiar with theoretical fundamentals for this book to make sense). Hence it is a first course in bayesian statistics, and not a first course in statistical theory. Particularly, you should definitely be very comfortable with Bayes' rule, and how to manipulate around probability statements using Bayes' rule and definitions of conditional probability fairly seamlessly, before giving the book a try as it assumes you are comfortable with those concepts going in. Also, experience with frequentist inference would be useful for putting Bayesian inference into perspective.

This is an excellent book but will be easier to understand if you have a M.S. level knowledge of classical (frequentist) statistics. It is clear, concise, and has good examples.

This book is a smooth introduction to the concept of Bayesian statistics. Anyone with fundamental level knowledge of college statistics could follow. The only thing which could be added to the current version is more detailed Appendix about common distributions and their conjugates.

Very helpful in understanding the fundamentals of Bayesian statistics!

Extremely overpriced! I had to pay $60 for a paperback, and there are still TYPOs in the book? Are you kidding me? The publishing industry overcharges, but never fails to deliver in quality.However, the book is excellent for it's content, particularly for someone who is unfamiliar with Bayesian stats and only intermediary familiarity with probability distributions. The concepts are well explained with examples . However, this is not a standalone book, and probably Gelman's book is worth looking at along with this.

Better quality than advertised!