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    Metadata details:
    filename: spm_MDP_VB_prune.m
    path of file: ./repos/spm/toolbox/DEM
    
    the code of the file until where you have to start completion: % Bayesian model reduction subroutine
%==========================================================================
function [qA,pA] = spm_MDP_VB_prune(qA,pA,f,T,C,OPT)
% FORMAT [sA,rA] = spm_MDP_VB_prune(qA,pA,f,T,C,OPT)
% qA  - posterior expectations
% pA  - prior expectations
% T   - threshold for Occam's window (natural units) [default: 2]
% f   - hidden factor to contract over [default: 0]
% C   - log preferences
% OPT - 'MI' or 'SIMPLE'
%
% sA  - reduced posterior expectations
% rA  - reduced prior expectations
%
% OPT - 'MI': mutual information
%
% This implementation compares models (i.e., prior concentration
% parameters) that change in the direction of maximising expected free
% energy; namely, maximising the mutual information entailed by a
% likelihood mapping or transition matrix (plus the log preference over
% outcomes or states). If the reduced prior exceeds the specified Occams
% window, in terms of the reduced free energy, the reduced  priors and
% posteriors replace the full priors and posteriors. Effectively, this
% implements the structural hyperprior that likelihood mappings with a high
% mutual information are plausible and accepts these new pr
    
function [qA,pA] = spm_MDP_VB_prune(qA,pA,f,T,C,OPT)
% FORMAT [sA,rA] = spm_MDP_VB_prune(qA,pA,f,T,C,OPT)
% qA  - posterior expectations
% pA  - prior expectations
% T   - threshold for Occam's window (natural units) [default: 2]
% f   - hidden factor to contract over [default: 0]
% C   - log preferences
% OPT - 'MI' or 'SIMPLE'
%
% sA  - reduced posterior expectations
% rA  - reduced prior expectations
%
% OPT - 'MI': mutual information
%
% This implementation compares models (i.e., prior concentration
% parameters) that change in the direction of maximising expected free
% energy; namely, maximising the mutual information entailed by a
% likelihood mapping or transition matrix (plus the log preference over
% outcomes or states). If the reduced prior exceeds the specified Occams
% window, in terms of the reduced free energy, the reduced  priors and
% posteriors replace the full priors and posteriors. Effectively, this
% implements the structural hyperprior that likelihood mappings with a high
% mutual information are plausible and accepts these new priors if there is
% sufficient evidence for them. This can be regarded as a generic form of
% structure learning.

% OPT - 'SIMPLE':  simple suppression of implausible likelihoods
%
% This option suppresses entries of the likelihood tensor with small values
% and places the posterior probability mass over more likely outcomes. In
% effect,  it end