Bayesian Nonparametrics

Cambridge Series in Statistical and Probabilistic Mathematics #28

by Nils Lid Hjort (editor), Peter Müller (Editor), Chris Holmes (Editor)

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 Prunster, 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.

Kindle Edition

First published January 1, 2010

Reviews

[Nils Lid Hjort · Rating 5 out of 5]

I'm one of the authors and hence by definition biased. I do, however, both like the book, admire the other chapters, by my colleagues, and recommend it. It may serve as a pretty wide and general introduction to both the theory and the far-ranging applications of Bayesian Nonparametrics. I think Cambridge University Press flagged it as "the coolest book on the hottest topic of statistics" when it came out in 2008.

A mild caveat ought to be that it's not easy to teach from. When I've taught BNP, at the Department of Mathematics at the University of Oslo (and for 3-day minicourses at other universities), I've used one or two chapters and supplemented these with course notes on my own and other articles.

If you are interested in BNP, taught from the level of "usual" Bayesian statistics and then upwards, you may check my course webpage from Autumn 2019, and also check the FocuStat webpage for further material, papers, blog posts stories, etc.

https://www.uio.no/studier/emner/matn...

[Nils Lid Hjort · Rating 5 out of 5]

I'm one of the authors and hence by definition biased. I do, however, both like the book, admire the other chapters, by my colleagues, and recommend it. It may serve as a pretty wide and general introduction to both the theory and the far-ranging applications of Bayesian Nonparametrics. I think Cambridge University Press flagged it as "the coolest book on the hottest topic of statistics" when it came out in 2008.

A mild caveat ought to be that it's not easy to teach from. When I've taught BNP, at the Department of Mathematics at the University of Oslo (and for 3-day minicourses at other universities), I've used one or two chapters and supplemented these with course notes on my own and other articles.

If you are interested in BNP, taught from the level of "usual" Bayesian statistics and then upwards, you may check my course webpage from Autumn 2019, and also check the FocuStat webpage for further material, papers, blog posts stories, etc.

https://www.uio.no/studier/emner/matn...