Abstract：
:In neuroimaging it is now becoming standard practise to fit multiple models to data and compare them using a model selection criterion. This is especially prevalent in the analysis of brain connectivity. This paper describes a simulation study which compares the relative merits of three model selection criteria (i) Akaike's Information Criterion (AIC), (ii) the Bayesian Information Criterion (BIC) and (iii) the variational Free Energy. Differences in performance are examined in the context of General Linear Models (GLMs) and Dynamic Causal Models (DCMs). We find that the Free Energy has the best model selection ability and recommend it be used for comparison of DCMs.
journal_name
Neuroimagejournal_title
NeuroImageauthors
Penny WDdoi
10.1016/j.neuroimage.2011.07.039subject
Has Abstractpub_date
2012-01-02 00:00:00pages
319-30issue
1eissn
1053-8119issn
1095-9572pii
S1053-8119(11)00816-0journal_volume
59pub_type
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