- Series: Cambridge Series in Statistical and Probabilistic Mathematics (Book 28)
- Hardcover: 308 pages
- Publisher: Cambridge University Press; 1 edition (April 12, 2010)
- Language: English
- ISBN-10: 0521513464
- ISBN-13: 978-0521513463
- Product Dimensions: 7 x 0.9 x 10 inches
- Shipping Weight: 1.9 pounds (View shipping rates and policies)
- Average Customer Review: 2 customer reviews
- Amazon Best Sellers Rank: #1,843,848 in Books (See Top 100 in Books)
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Bayesian Nonparametrics (Cambridge Series in Statistical and Probabilistic Mathematics) 1st Edition
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"The book looks like it will be useful to a wide range of researchers. I like that there is a lot of discussion of the models themselves as well as the computation. The book, especially in the early chapters, is more theoretical than I would prefer... But, hey, that's just my taste... on the whole I think the book is excellent. If I didn't think the book was important, I wouldn't be spending my time pointing out my disagreements with it!"
Andrew Gelman, Columbia University
"The book provides a tour de force presentation of selected topics in an emerging branch of modern statistical science, and not only justfies the reader's curiosity, but also expands it.... The book brings together a well-structured account of a number of topics on the theory, methodology, applications, and challenges of future developments in the rapidly expanding area of Bayesian nonparametrics. Given the current dearth of books on BNP, this book will be an invaluable source of information and reference for anyone interested in BNP, be it a student, an established statistician, or a researcher in need of flexible statistical analyses."
Milovan Krnjajic, Journal of the American Statistical Association
Bayesian nonparametrics works. Applications are appearing in such disciplines as information retrieval, NLP, machine vision, computational biology, cognitive science, signal processing. In this coherent introduction, the editors weave together tutorial chapters by Ghosal, Lijoi and Prünster, Dunson, and Teh and Jordan, giving direct access to these exciting ideas and methods.
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There is actually a *lot* of material available online regarding NP Bayesian material, but it does not seem to be documented in book form. I know that now after applying these methods to some problems. So, I recalibrate.
Relative to what else is available, this book is good. It could use more and extended practical examples worked throughout the book. The book *does* cite such examples in the literature, but it doesn't show their particulars.
The thing is, I find to apply Bayesian methods there are lots of engineering hints needed. You'll find these in good books, like *The* *BUGS* *Book* by Lunn, Jackson, Best, Thomas, and Spiegelhalter, or Kruschke's *Doing* *Bayesian* *Data* *Analysis* or Congdon's *Bayesian Statistical Modelling*.