Dear developers and users,
I currently try to compare several DCM models fitted to fMRI data
collected from several subjects. The concept of Bayesian model
selection is clear to me, however, I find the piece of code that
computes the posterior probabilities of different models of a subject
pretty mysterious. I'm referring to this part:
% compute conditional probability of DCMs under flat priors.
%--------------------------------------------------------------------------
F = evidence - min(evidence);
i = F < (max(F) - 32);
P = F;
P(i) = max(F) - 32;
P = P - min(P);
P = exp(P);
P = P/sum(P);
Is there any documentation available about the theoretical idea behind
this computation?
My other question is that where can I find out what happens if I use
the graphical interface to compare the models using the data from
several subjects. If I do this, I get very different results than if I
compute the model probabilities and log-evidences separately for every
subject and average them out.
Thank you for your help,
Mihály Bányai
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