Bayesian Nonparametrics (Electronic book text)


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 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.

Delivery AdviceNot available

Toggle WishListAdd to wish list
Review this Item

Product Description

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 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.

Customer Reviews

No reviews or ratings yet - be the first to create one!

Product Details

General

Imprint

Cambridge University Press (Virtual Publishing)

Country of origin

United Kingdom

Series

Cambridge Series in Statistical and Probabilistic Mathematics

Release date

April 2011

Availability

We don't currently have any sources for this product. If you add this item to your wish list we will let you know when it becomes available.

Editors

, , ,

Format

Electronic book text

ISBN-13

978-0-511-66926-2

Barcode

9780511669262

Categories

LSN

0-511-66926-7



Trending On Loot