Comparing families of dynamic causal models

Penny, Will D., Stephan, Klaas E, Daunizeau, Jean, Rosa, Maria J, Friston, Karl J, Schofield, Thomas M and Leff, Alex P (2010) Comparing families of dynamic causal models. PLoS Computational Biology, 6 (3). ISSN 1553-734X

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Abstract

Mathematical models of scientific data can be formally compared using Bayesian model evidence. Previous applications in the biological sciences have mainly focussed on model selection in which one first selects the model with the highest evidence and then makes inferences based on the parameters of that model. This "best model" approach is very useful but can become brittle if there are a large number of models to compare, and if different subjects use different models. To overcome this shortcoming we propose the combination of two further approaches: (i) family level inference and (ii) Bayesian model averaging within families. Family level inference removes uncertainty about aspects of model structure other than the characteristic of interest. For example: What are the inputs to the system? Is processing serial or parallel? Is it linear or nonlinear? Is it mediated by a single, crucial connection? We apply Bayesian model averaging within families to provide inferences about parameters that are independent of further assumptions about model structure. We illustrate the methods using Dynamic Causal Models of brain imaging data.

Item Type: Article
Uncontrolled Keywords: bayes theorem,brain,causality,computer simulation,neurological models,statistical models,nerve net,automated pattern recognition
Faculty \ School: Faculty of Social Sciences > School of Psychology
Depositing User: Pure Connector
Date Deposited: 19 Aug 2017 06:06
Last Modified: 25 Jul 2018 13:58
URI: https://ueaeprints.uea.ac.uk/id/eprint/64596
DOI: 10.1371/journal.pcbi.1000709

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