An invitation to Bayesian nonparametrics Nils Lid Hjort, Chris Holmes, Peter Müller and Stephen G. Walker; 1. Bayesian nonparametric methods: motivation and ideas Stephen G. Walker; 2. The Dirichlet process, related priors, and posterior asymptotics Subhashis Ghosal; 3. Models beyond the Dirichlet process Antonio Lijoi and Igor Prünster; 4. Further models and applications Nils Lid Hjort; 5. Hierarchical Bayesian nonparametric models with applications Yee Whye Teh and Michael I. Jordan; 6. Computational issues arising in Bayesian nonparametric hierarchical models Jim Griffin and Chris Holmes; 7. Nonparametric Bayes applications to biostatistics David B. Dunson; 8. More nonparametric Bayesian models for biostatistics Peter Müller and Fernando Quintana; Author index; Subject index.
The most intelligent guide to the hottest field in statistics.
Nils Lid Hjort is Professor of Mathematical Statistics in the Department of Mathematics at the University of Oslo. Chris Holmes is Professor of Biostatistics in the Department of Statistics at the University of Oxford. He has been awarded the Guy Medal in Bronze for 2009 by the Royal Statistical Society. Peter Müller is Professor in the Department of Biostatistics at the University of Texas M. D. Anderson Cancer Center. Stephen G. Walker is Professor of Statistics in the Institute of Mathematics, Statistics and Actuarial Science at the University of Kent, Canterbury.
"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
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