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Comparing families of dynamic causal models

Penny, W.D.; Stephan, Klaas E.; Daunizeau, J.; Rosa, M.J.; Friston, K.J.; Schofield, T.M.; Leff, A.P.; (2010) Comparing families of dynamic causal models. PLoS Computational Biology , 6 (3) , Article e1000709. 10.1371/journal.pcbi.1000709. Green open access

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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.

Type: Article
Title: Comparing families of dynamic causal models
Open access status: An open access version is available from UCL Discovery
DOI: 10.1371/journal.pcbi.1000709
Publisher version: http://dx.doi.org/10.1371/journal.pcbi.1000709
Language: English
Additional information: © 2010 Penny et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
UCL classification: UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Imaging Neuroscience
URI: https://discovery.ucl.ac.uk/id/eprint/20052
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