The Connection between Bayesian Inference and Information Theory for Model Selection, Information Gain and Experimental Design

The Connection between Bayesian Inference and Information Theory for Model Selection, Information Gain and Experimental Design

We show a link between Bayesian inference and information theory that is useful for model selection, assessment of information entropy and experimental design. We align Bayesian model evidence (BME) with relative entropy and cross entropy in order to simplify computations using prior-based (Monte Ca...

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Bibliographic Details
Main Authors: Sergey Oladyshkin, Wolfgang Nowak
Format: Article
Language:English
Published: MDPI AG 2019-11-01
Series:Entropy
Subjects:
model evidence, entropy, model selection, information entropy, bayesian experimental design, kullback–leibler divergence, markov chain monte carlo, monte carlo
Online Access:https://www.mdpi.com/1099-4300/21/11/1081

Internet

https://www.mdpi.com/1099-4300/21/11/1081

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