Learning Functions and Approximate Bayesian Computation Design: ABCD

Learning Functions and Approximate Bayesian Computation Design: ABCD

A general approach to Bayesian learning revisits some classical results, which study which functionals on a prior distribution are expected to increase, in a preposterior sense. The results are applied to information functionals of the Shannon type and to a class of functionals based on expected dis...

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Bibliographic Details
Main Authors: Markus Hainy, Werner G. Müller, Henry P. Wynn
Format: Article
Language:English
Published: MDPI AG 2014-08-01
Series:Entropy
Subjects:
learning
Shannon information
majorization
optimum experimental design
approximate Bayesian computation
Online Access:http://www.mdpi.com/1099-4300/16/8/4353

Internet

http://www.mdpi.com/1099-4300/16/8/4353

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