<p>Cross-validated Bayesian model averaging: how to improve parameter estimates in GLM-based fMRI data analysis</p>

Joram Soch; Achim Pascal Meyer; John-Dylan Haynes; Carsten Allefeld

doi:10.7490/f1000research.1114774.1

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Soch J, Meyer AP, Haynes JD and Allefeld C. Cross-validated Bayesian model averaging: how to improve parameter estimates in GLM-based fMRI data analysis [version 1; not peer reviewed]. F1000Research 2017, 6:1538 (poster) (https://doi.org/10.7490/f1000research.1114774.1)

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Cross-validated Bayesian model averaging: how to improve parameter estimates in GLM-based fMRI data analysis

Joram Soch¹, Achim Pascal Meyer, John-Dylan Haynes, Carsten Allefeld

Published 22 Aug 2017 (https://doi.org/10.7490/f1000research.1114774.1)

Author Affiliations

¹ Bernstein Center for Computational Neuroscience, Berlin, Germany

Copyright © 2017 Soch J et al. This is an open access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Published 22 Aug 2017

Cross-validated Bayesian model averaging: how to improve parameter estimates in GLM-based fMRI data analysis
[version 1; not peer reviewed]

Joram Soch¹, Achim Pascal Meyer, John-Dylan Haynes, Carsten Allefeld

Author Affiliations

¹ Bernstein Center for Computational Neuroscience, Berlin, Germany

Presented at

23rd Annual Meeting of the Organization for Human Brain Mapping (OHBM) 2017

Abstract

Competing Interests

No competing interests were disclosed

Keywords

fMRI, GLM, SPM, model misspecification, multiple model estimation, cross-validation, Bayesian model averaging, nested models, model complexity, false positives, false negatives

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