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Increasing generalizability via the principle of minimum description length

Published online by Cambridge University Press:  10 February 2022

Wes Bonifay Open the ORCID record for Wes Bonifay [Opens in a new window]
Wes Bonifay*
Affiliation:
Missouri Prevention Science Institute, University of Missouri, Columbia, MO65211, USAbonifayw@missouri.eduhttps://education.missouri.edu/person/wes-bonifay/
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Abstract

Traditional statistical model evaluation typically relies on goodness-of-fit testing and quantifying model complexity by counting parameters. Both of these practices may result in overfitting and have thereby contributed to the generalizability crisis. The information-theoretic principle of minimum description length addresses both of these concerns by filtering noise from the observed data and consequently increasing generalizability to unseen data.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 45 , 2022 , e5
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Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press

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