Bayesian Nonparametrics: 28 (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 28) - Hardcover
Book 20 of 46: Cambridge Series in Statistical and Probabilistic Mathematics
Synopsis
The most intelligent guide to the hottest field in statistics.
"synopsis" may belong to another edition of this title.
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 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.
"About this title" may belong to another edition of this title.
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Bayesian Nonparametrics
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Bayesian Nonparametrics
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Hardcover. Condition: new. Hardcover. 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 Pruenster, 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. 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 Pruenster, Dunson, and Teh and Jordan, giving direct access to these exciting ideas and methods. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9780521513463
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Bayesian Nonparametrics (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 28)
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Hardback. Condition: New. 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. Seller Inventory # LU-9780521513463
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