Dear Sadjad,
It's not really EM - it's Variational Bayes. For the equations used in DCM, see the appendix of this paper by Will Penny - http://www.sciencedirect.com/science/article/pii/S1053811911008160 .
For the more general theory, which is where I would start, there's a very nice step by step tutorial for the simpler case of a 1-dimensional Gaussian on the Wikipedia - https://en.wikipedia.org/wiki/Variational_Bayesian_methods . Alternatively, there's this tutorial, which isn't about DCM per se but is relatively clear - http://www.sciencedirect.com/science/article/pii/S0022249614000352 .
Best
Peter.
-----Original Message-----
From: SPM (Statistical Parametric Mapping) [mailto:[log in to unmask]] On Behalf Of Sadjad Sadeghi
Sent: 17 November 2016 15:39
To: [log in to unmask]
Subject: [SPM] EM algorithm for DCM in details
Dear DCM experts,
I am trying to understand the EM algorithm which is used in DCM for estimating the parameters, but I really have some problems in deriving the details of different steps especially in the M-step (specifically p. 480 Friston et.al 2002, classical and bayesian inference in neuroimaging: theory) Does anybody know a reference to explain this steps in more details? I more mean the exact equations which are used in DCM, not the general methods. Thank you so much in advance.
Best regards,
Sadjad Sadeghi
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