Bayesian Nonparametrics Nils Lid Hjort, Chris Holmes, Peter Müller, Stephen G. Walker (Editors) Cambridge University Press, , viii +. Nils Lid Hjort. University of Oslo. 1 Introduction and summary. The intersection set of Bayesian and nonparametric statistics was almost empty until about Bayesian Nonparametrics edited by Nils Lid Hjort, Chris Holmes, Peter Müller, Stephen G. Walker. Nils Hjort. Author. Nils Hjort. International Statistical Review.
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Computational issues, though challenging, are no longer intractable. All that is needed is an entry point: 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. The Best Books of Check out the top books of the year on our page Best Books of Looking for beautiful books? Visit our Beautiful Books page and find lovely books for kids, photography lovers and more. Other books in this series.
Nils Lid Hjort
Data Analysis and Graphics Using R: Numerical Methods of Statistics John F. The Dirichlet process, related priors, and posterior asymptotics Subhashis Ghosal; 3.
Further models and applications Nils Lid Hjort; 5. Computational issues arising in Bayesian nonparametric hierarchical models Jim Griffin and Chris Holmes; 7.
Nils Lid Hjort – Department of Mathematics
Nonparametric Bayes applications to biostatistics David B. Review Text “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 If I didn’t think the book was important, I wouldn’t be spending my time pointing out my disagreements with it!
Review quote “The book looks like it will be useful to a wide range of researchers. 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.
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