Patterns of scalable Bayesian inference

Patterns of Scalable Bayesian Inference seeks to identify unifying principles, patterns, and intuitions for scaling Bayesian inference. It reviews existing work on utilizing modern computing resources with both MCMC and variational approximation techniques and comments on the path forward.

Detalles Bibliográficos
Autores principales: Angelino, Elaine, Johnson, Matthew James, Adams, Ryan P
Lenguaje:eng
Publicado: Now Publishers 2016
Materias:
XX
Acceso en línea:http://cds.cern.ch/record/2762168

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