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Bayesian Philosophy of Science$
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Jan Sprenger and Stephan Hartmann

Print publication date: 2019

Print ISBN-13: 9780199672110

Published to Oxford Scholarship Online: October 2019

DOI: 10.1093/oso/9780199672110.001.0001

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Simplicity and Model Selection

Simplicity and Model Selection

Chapter:
(p.261) Variation 10: Simplicity and Model Selection
Source:
Bayesian Philosophy of Science
Author(s):

Jan Sprenger

Stephan Hartmann

Publisher:
Oxford University Press
DOI:10.1093/oso/9780199672110.003.0010

Is simplicity a virtue of a good scientific theory, and are simpler theories more likely to be true or predictively successful? If so, how much should simplicity count vis-à-vis predictive accuracy? We address this question using Bayesian inference, focusing on the context of statistical model selection and an interpretation of simplicity via the degree of freedoms of a model. We rebut claims to prove the epistemic value of simplicity by means of showing its particular role in Bayesian model selection strategies (e.g., the BIC or the MML). Instead, we show that Bayesian inference in the context of model selection is usually done in a philosophically eclectic, instrumental fashion that is more tuned to practical applications than to philosophical foundations. Thus, these techniques cannot justify a particular “appropriate weight of simplicity in model selection”.

Keywords:   Statistical model selection, Bayesian model selection, Bayesian Information Criterion (BIC), simplicity, instrumental Bayesianism, statistical models

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