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    Metadata details:
    filename: DEMO_DCM_PEB_REC.m
    path of file: ./repos/bspm/thirdparty/spm12/toolbox/DEM
    
    the code of the file until where you have to start completion: % function DEMO_DCM_PEB_REC
% Test routine to check group DCM for electrophysiology
%--------------------------------------------------------------------------
% This routine illustrates the use of Bayesian model reduction when
% inverting hierarchical (dynamical) models; for example, multisubject DCM
% models. In this demonstration empirical Bayesian model reduction is
% applied recursively in an attempt to finesse the local  minimum problem
% with a nonlinear DCMs. The estimates are compared against standard
% Bayesian model reduction, in terms of the subject specific estimates and
% Bayesian model comparison  (and averages) at the between subject level.
% 
% First, an EEG DCM (using empirical grand mean data) is inverted to
% find plausible group mean parameters. Single subject data are
% then generated using typical within and between subject variance (here, 
% group differences in the modulation of intrinsic connectivity. We then
% illustrate empirical Bayesian model reduction in a recursive mode.
%
% See also: DEMO_DCM_PEB.m
%__________________________________________________________________________
% Copyright (C) 2015 Wellcome Trust Centre for Neuroimaging

% Karl Friston, Peter Zeidman
% $Id: DEMO_DCM_PEB_REC.m 6737 2016-03-03 12:05:51Z karl $


% change to directory with empirical data
%--------------------------------------------------------------------------
%   options.analysis     - 'ERP','CSD', 'IND' or 'TFM
%   options.model        - 'ERP','SEP','CMC','LFP','NNM' or 'MFM'
%   options.spatial      - 'ECD','LFP' or 'IMG'
%--------------------------------------------------------------------------
try
    cd(fullfile(spm('Dir'),'tests','data','MEEG'))
catch
    cd('C:\Users\karl\Documents\SPM\DCM tests')
end
close all, clear all
clc
rng('default')

% set up
%==========================================================================
load DCM_MMN                               % base DCM

DCM.options.spatial  = 'ECD';
DCM.options.analysis = 'ERP';
DCM.options.model    = 'ERP';
DCM.options.Nmax     = 32
    
function DEMO_DCM_PEB_REC
% Test routine to check group DCM for electrophysiology
%--------------------------------------------------------------------------
% This routine illustrates the use of Bayesian model reduction when
% inverting hierarchical (dynamical) models; for example, multisubject DCM
% models. In this demonstration empirical Bayesian model reduction is
% applied recursively in an attempt to finesse the local  minimum problem
% with a nonlinear DCMs. The estimates are compared against standard
% Bayesian model reduction, in terms of the subject specific estimates and
% Bayesian model comparison  (and averages) at the between subject level.
% 
% First, an EEG DCM (using empirical grand mean data) is inverted to
% find plausible group mean parameters. Single subject data are
% then generated using typical within and between subject variance (here, 
% group differences in the modulation of intrinsic connectivity. We then
% illustrate empirical Bayesian model reduction in a recursive mode.
%
% See also: DEMO_DCM_PEB.m
%__________________________________________________________________________
% Copyright (C) 2015 Wellcome Trust Centre for Neuroimaging

% Karl Friston, Peter Zeidman
% $Id: DEMO_DCM_PEB_REC.m 6737 2016-03-03 12:05:51Z karl $


% change to directory with empirical data
%--------------------------------------------------------------------------
%   options.analysis     - 'ERP','CSD', 'IND' or 'TFM
%   options.model        - 'ERP','SEP','CMC','LFP','NNM' or 'MFM'
%   options.spatial      - 'ECD','LFP' or 'IMG'
%--------------------------------------------------------------------------
try
    cd(fullfile(spm('Dir'),'tests','data','MEEG'))
catch
    cd('C:\Users\karl\Documents\SPM\DCM tests')
end
close all, clear all
clc
rng('default')

% set up
%==========================================================================
load DCM_MMN                               % base DCM

DCM.options.spatial  = 'ECD';
DCM.options.analysis = 'ERP';
DCM.options.model    = 'ERP';
DCM.options.Nmax     = 32;
DCM.options.DATA     = 1;
DCM.name             = 'DCM_GROUP';

% model space - within subject effects
%--------------------------------------------------------------------------
k     = spm_perm_mtx(3);
for i = 1:8;
    B{i}     = sparse(5,5);
    if k(i,1)
        B{i} = B{i} + sparse([1 2 3 4],[1 2 3 4],1,5,5);
    end