Title: Bayesian Nonparametrics (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 28)
Publisher: Cambridge University Press
Publication Date: 2010
Language: English
ISBN 10: 0521513464
ISBN 13: 9780521513463
Binding: Hardcover
Condition: New

About this title

Synopsis

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 Pr�nster, 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.

About the Authors

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 M�ller 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.

"About this title" may belong to another edition of this title.

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