Bayesian nonparametrics works - theoretically, computationally. The theory provides highly flexible models whose complexity grows appropriately with the amount of data. Computational issues, though challenging, are no longer intractable. All that is needed is an entry point: this intelligent book is the perfect guide to what can seem a forbidding landscape. Tutorial chapters by Ghosal, Lijoi and Prünster, Teh and Jordan, and Dunson advance from theory, to basic models and hierarchical modeling, to applications and implementation, particularly in computer science and biostatistics. These are complemented by companion chapters by the editors and Griffin and Quintana, providing additional models, examining computational issues, identifying future growth areas, and giving links to related topics. This coherent text gives ready access both to underlying principles and to state-of-the-art practice. Specific examples are drawn from information retrieval, NLP, machine vision, computational biology, biostatistics, and bioinformatics.
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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
Andrew Gelman, Columbia University
Book Description
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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7 of 8 people found the following review helpful:
2.0 out of 5 stars
not an entry-level book,
By stats guy "stats guy" (Pasadena, CA) - See all my reviews
This review is from: Bayesian Nonparametrics (Cambridge Series in Statistical and Probabilistic Mathematics) (Hardcover)
This book was derived from a summer school/conference, and shows it. The chapters read like lecture notes (but without the verbal interjections and description which would surely have accompanied the text). As it is, the chapters are very dense and terse, and not comfortable to read in a standalone way. Also, the mathematical notation varies from chapter to chapter. I would not recommend this.
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