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A First Course in Bayesian Statistical Methods (Springer Texts in Statistics)
Free Download A First Course in Bayesian Statistical Methods (Springer Texts in Statistics)
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A self-contained introduction to probability, exchangeability and Bayes’ rule provides a theoretical understanding of the applied material. Numerous examples with R-code that can be run "as-is" allow the reader to perform the data analyses themselves. The development of Monte Carlo and Markov chain Monte Carlo methods in the context of data analysis examples provides motivation for these computational methods.
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Product details
Series: Springer Texts in Statistics
Hardcover: 271 pages
Publisher: Springer; 1st ed. 2009 edition (July 14, 2009)
Language: English
ISBN-10: 0387922997
ISBN-13: 978-0387922997
Product Dimensions:
6.1 x 0.7 x 9.2 inches
Shipping Weight: 1.2 pounds (View shipping rates and policies)
Average Customer Review:
3.7 out of 5 stars
18 customer reviews
Amazon Best Sellers Rank:
#220,044 in Books (See Top 100 in Books)
This book is not a first course book.... I did stats for two years before trying this book and could not get through it. I've been in stats courses for three years and even now get stuck whenever I try this book. It's just not a first course level/appropriate level for the title.It had good programming tips, but that's what redeems it.As for a first course in Bayesian, try kruschke's book with the puppies. It's much simpler to get through.
The text is fine, although as another reviewer mentioned - the homework is frustrating. None of it is the sort of problem where you can go back and follow along with work done in the chapter. Not at all good for someone who prefers to learn by first mimicking, and then exploring.No, the BIG problem is that many of the formulas are stored as images since equations are incompatible with the file format. If you try to increase the text size so you can actually read them, the formulas stay the same tiny size. Trying to use the windows accessibility tools just results in a pixelated illegible formula.I would love to have all my texts in electronic format, but not until this issue can be fixed.
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.
This is an appropriate practical introduction to Bayesian methods for someone who has taken both a college-level probability and statistics course. The multidimensional examples may require a bit of linear algebra. It doesn't include much comparison with frequentist techniques, so some familiarity there would help the reader put the ideas in context.Compared to a book like Christian Robert's excellent _The Bayesian Choice_, this book may appear inadequate, because it is less than half the size, is often less dense and scholarly, and is (currently at Amazon) almost double the price. However, I'm happy I have both because Hoff's book is more practical for someone who actually wants to use Bayesian statistics in practical situations. Hoff spends a lot of time discussing simple examples with wide application, and he actually shows the R code to compute the answers with MCMC techniques.However, after reading the book, I still don't feel totally prepared to apply R in real-life Bayesian situations. It would be nice for a practical book like Hoff's to include some hands-on tips about how to do these problems (R packages to use, basic modeling strategies, common pitfalls, speed concerns, assessing convergence, etc.).
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.
Peter does a great job explaining material clearly. However, I really wish he offered solutions to problems in the back of the book. This would make self-learning easier.
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.
The book is used for my Bayesian stats class. I think it's easy to read and very clear. But you should make sure you do have some prior knowledge about statistics and probability, because the author takes it as granted.
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