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Bayes beyond the predictive distribution

Published online by Cambridge University Press:  23 September 2024

Anna Székely Open the ORCID record for Anna Székely [Opens in a new window]  and
Gergő Orbán Open the ORCID record for Gergő Orbán [Opens in a new window]
Anna Székely*
Affiliation:
Department of Computational Sciences, HUN-REN Wigner Research Centre for Physics, Budapest, Hungary [email protected] [email protected] http://golab.wigner.mta.hu/people/anna-szekely/ http://golab.wigner.mta.hu/people/gergo-orban/ Department of Cognitive Science, Faculty of Natural Sciences, Budapest University of Technology and Economics, Budapest, Hungary
Gergő Orbán
Affiliation:
Department of Computational Sciences, HUN-REN Wigner Research Centre for Physics, Budapest, Hungary [email protected] [email protected] http://golab.wigner.mta.hu/people/anna-szekely/ http://golab.wigner.mta.hu/people/gergo-orban/
*
*Corresponding author.
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Abstract

Binz et al. argue that meta-learned models offer a new paradigm to study human cognition. Meta-learned models are proposed as alternatives to Bayesian models based on their capability to learn identical posterior predictive distributions. In our commentary, we highlight several arguments that reach beyond a predictive distribution-based comparison, offering new perspectives to evaluate the advantages of these modeling paradigms.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 47 , 2024 , e166
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Copyright
Copyright © The Author(s), 2024. Published by Cambridge University Press

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