You are an expert in writing code in many different languages. Your goal is to perform code completion for the following code, keeping in mind the rest of the code and the file meta data.
    Metadata details:
    filename: VBA_MFX.m
    path of file: ./repos/VBA-toolbox
    
    the code of the file until where you have to start completion: function [p_sub,o_sub,p_group,o_group] = VBA_MFX(y,u,f_fname,g_fname,dim,options,priors_group, options_group)
% VB treatment of mixed-effects analysis
% function [posterior,out] = VBA_MFX(y,u,f_fname,g_fname,dim,options)
% This function approaches model inversion from an empirical Bayes
% perspective, whereby within-subject priors are iteratively refined and
% matched to the inferred parent population distribution.
%  Note: all subjects must use the same model
% IN:
%   - y: nsx1 cell array of observations, where ns is the number of
%   subjects in the group
%   - u:  nsx1 cell array of inputs
%   - f_fname/g_fname: evolution/observation function handles
%   - dim: structure containing the model dimensions.
%   - options: nsx1 cell array of options structure. Note: if specified
%   here, the priors on observation and evolution parameters (as well as
%   initial conditions) are useless, since they are replaced by empirical
%   Bayes priors. Priors on precision hyperparameters however, are not
%   treated as random effects drawn from a parent population distribution.
%   In turn, MFX analysis does not update their moments using group-level
%   information...
%   - priors_group: structure containing the prior sufficient statistics on
%   the moments of the parent population distributions (for observation and
%   evolution parameters, as well as for initial conditions, if
%   applicable). See p_group subfields below.
%   - options_group: Structure containing options for MFX. Fields:
%     .TolFun - Minimum change in the free energy, default is 2e-2
%     .MaxIter - Maximum number of iterations, default is 16
%     .DisplayWin - Do we want a graphical output?

    
function [p_sub,o_sub,p_group,o_group] = VBA_MFX(y,u,f_fname,g_fname,dim,options,priors_group, options_group)
% VB treatment of mixed-effects analysis
% function [posterior,out] = VBA_MFX(y,u,f_fname,g_fname,dim,options)
% This function approaches model inversion from an empirical Bayes
% perspective, whereby within-subject priors are iteratively refined and
% matched to the inferred parent population distribution.
%  Note: all subjects must use the same model
% IN:
%   - y: nsx1 cell array of observations, where ns is the number of
%   subjects in the group
%   - u:  nsx1 cell array of inputs
%   - f_fname/g_fname: evolution/observation function handles
%   - dim: structure containing the model dimensions.
%   - options: nsx1 cell array of options structure. Note: if specified
%   here, the priors on observation and evolution parameters (as well as
%   initial conditions) are useless, since they are replaced by empirical
%   Bayes priors. Priors on precision hyperparameters however, are not
%   treated as random effects drawn from a parent population distribution.
%   In turn, MFX analysis does not update their moments using group-level
%   information...
%   - priors_group: structure containing the prior sufficient statistics on
%   the moments of the parent population distributions (for observation and
%   evolution parameters, as well as for initial conditions, if
%   applicable). See p_group subfields below.
%   - options_group: Structure containing options for MFX. Fields:
%     .TolFun - Minimum change in the free energy, default is 2e-2
%     .MaxIter - Maximum number of iterations, default is 16
%     .DisplayWin - Do we want a graphical output?
%     .verbose - Do we want verbose text output?
%
% OUT:
%   - p_sub/o_sub: nsx1 cell arrays containng the VBA outputs of the
%   within-subject model inversions.
%   - p_group: structure containing the sufficient statistics of the
%   posterior over the moments of the parent population distribution. Its
%   subfields are:
%       .muPhi/SigmaPhi: VB sufficient statistics (first 2 moments) of the
%       Gaussian posterior pdf over the population mean of observation
%       parameters.
%       .muTheta/SigmaTheta: [id] for evolution parameters.
%       .muX0/SigmaX0: [id] for initial conditions.
%       .a_vPhi/b_vPhi: VB sufficient statistics (scale and shape
%       parameters) of the Gamma posterior pdf over the population
%       precision of observation parameters. NB: a_vPhi and b_vPhi have the
%       same dimension than muPhi!
%       .a_vTheta/b_vTheta: [id] for evolution parameters.
%       .a_vX0/b_vX0: [id] for initial conditions.
%   - o_group: output structure of the VBA_MFX approach. In particular, it
%   contains the following subfields:
%       .F: a vector of free energies (across VB iterations). Its last
%       entry (F(end