Name: Fundamentals of Nonparametric Bayesian Inference (Cambridge Series in Statistical and Probabilistic Mathematics) (en Inglés)
Price: 131630.00 CLP
Availability: InStock
Author: Ghosal, Subhashis ; Van Der Vaart, Aad
ISBN: 9780521878265

portada Fundamentals of Nonparametric Bayesian Inference (Cambridge Series in Statistical and Probabilistic Mathematics) (en Inglés)

Fundamentals of Nonparametric Bayesian Inference (Cambridge Series in Statistical and Probabilistic Mathematics) (en Inglés)

Formato

Libro Físico

Autor

Subhashis Ghosal

Aad Van Der Vaart

Editorial

Cambridge University Press

Año

2017

Idioma

Inglés

N° páginas

670

Encuadernación

Tapa Dura

Dimensiones

26.5 x 18.8 x 4.2 cm

Peso

1.38 kg.

ISBN13

9780521878265

N° edición

Categorías

Probabilidad y estadística

Fundamentals of Nonparametric Bayesian Inference (Cambridge Series in Statistical and Probabilistic Mathematics) (en Inglés)

Subhashis Ghosal (Autor) · Aad Van Der Vaart (Autor) · Cambridge University Press · Tapa Dura

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Reseña del libro "Fundamentals of Nonparametric Bayesian Inference (Cambridge Series in Statistical and Probabilistic Mathematics) (en Inglés)"

Explosive growth in computing power has made Bayesian methods for infinite-dimensional models - Bayesian nonparametrics - a nearly universal framework for inference, finding practical use in numerous subject areas. Written by leading researchers, this authoritative text draws on theoretical advances of the past twenty years to synthesize all aspects of Bayesian nonparametrics, from prior construction to computation and large sample behavior of posteriors. Because understanding the behavior of posteriors is critical to selecting priors that work, the large sample theory is developed systematically, illustrated by various examples of model and prior combinations. Precise sufficient conditions are given, with complete proofs, that ensure desirable posterior properties and behavior. Each chapter ends with historical notes and numerous exercises to deepen and consolidate the reader's understanding, making the book valuable for both graduate students and researchers in statistics and machine learning, as well as in application areas such as econometrics and biostatistics.