Principles of Bayesian Inference Using General Divergence Criteria

Principles of Bayesian Inference Using General Divergence Criteria

When it is acknowledged that all candidate parameterised statistical models are misspecified relative to the data generating process, the decision maker (DM) must currently concern themselves with inference for the parameter value minimising the Kullback–Leibler (KL)-divergence between the...

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Bibliographic Details
Main Authors: Jack Jewson, Jim Q. Smith, Chris Holmes
Format: Article
Language:English
Published: MDPI AG 2018-06-01
Series:Entropy
Subjects:
Kullback–Leibler divergence
robustness
Bayesian updating
minimum divergence estimation
M-open inference
Online Access:http://www.mdpi.com/1099-4300/20/6/442

Internet

http://www.mdpi.com/1099-4300/20/6/442

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