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Bayesian Nonparametrics

12 View(s), published at 2010-04-12, written by Nils Lid Hjort, Chris Holmes, Peter Müller, Stephen G. Walker, published by Cambridge University Press
Bayesian Nonparametrics

Bayesian nonparametrics by Nils Lid Hjort, Chris Holmes, Peter Müller, Stephen G. Walker Bayesian nonparametrics works - theoretically, computationally. The theory provides highly flexible models whose complexity grows appropriately with the amount of data. Computational issues, though ch.... Published date on: 2010-04-12 with total page: pages. Publisher of Bayesian Nonparametrics is Cambridge University Press.

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.

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