Name: Patterns of Scalable Bayesian Inference (en Inglés)
Price: 367278.00 COP
Availability: InStock
Author: Angelino, Elaine ; Johnson, Matthew James ; Adams, Ryan P.
ISBN: 9781680832181

portada Patterns of Scalable Bayesian Inference (en Inglés)

Formato

Libro Físico

Autor

Elaine Angelino

Matthew James Johnson

Ryan P. Adams

Editorial

Now Publishers

Idioma

Inglés

N° páginas

148

Encuadernación

Tapa Blanda

Dimensiones

23.4 x 15.6 x 0.8 cm

Peso

0.22 kg.

ISBN13

9781680832181

Categorías

Teoría matemática de la computación
Machine learning: aprendizaje automático

Patterns of Scalable Bayesian Inference (en Inglés)

Elaine Angelino (Autor) · Matthew James Johnson (Autor) · Ryan P. Adams (Autor) · Now Publishers · Tapa Blanda

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Reseña del libro "Patterns of Scalable Bayesian Inference (en Inglés)"

Datasets are growing not just in size but in complexity, creating a demand for rich models and quantification of uncertainty. Bayesian methods are an excellent fit for this demand, but scaling Bayesian inference is a challenge. In response to this challenge, there has been considerable recent work based on varying assumptions about model structure, underlying computational resources, and the importance of asymptotic correctness. As a result, there is a zoo of ideas with a wide range of assumptions and applicability. Patterns of Scalable Bayesian Inference seeks to identify unifying principles, patterns, and intuitions for scaling Bayesian inference. It examines how these techniques can be scaled up to larger problems and scaled out across parallel computational resources. It reviews existing work on utilizing modern computing resources with both MCMC and variational approximation techniques. From this taxonomy of ideas, it characterizes the general principles that have proven successful for designing scalable inference procedures and addresses some of the significant open questions and challenges.