eBook, PDF

Bayesian Nonparametrics (eBook, PDF)

Beschreibung

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.

Technische Details

• Geräte: PC
• mit Kopierschutz
• Größe: 2.69MB
• FamilySharing(5)

Barrierefreiheit

• Text-to-Speech

Produktdetails

• Verlag: Cambridge University Press
• Erscheinungstermin: 12. April 2010
• Englisch
• ISBN-13: 9780511669262
• Artikelnr.: 37840939

Rezensionen

"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... on the whole I think the book is excellent. If I didn't think the book was important, I wouldn't be spending my time pointing out my disagreements with it!"
Andrew Gelman, Columbia University