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: spm_dcm_mdp.m
    path of file: ./repos/bspm/thirdparty/spm12
    
    the code of the file until where you have to start completion: function [DCM] = spm_dcm_mdp(DCM)
% MDP inversion using Variational Bayes
% FORMAT [DCM] = spm_dcm_mdp(DCM)
%
% Expects:
%--------------------------------------------------------------------------
% DCM.MDP   % MDP structure specifying a generative model
% DCM.field % parameter (field) names to optimise
% DCM.U     % cell array of outcomes (stimuli)
% DCM.Y     % cell array of responses (action)
%
% Returns:
%--------------------------------------------------------------------------
% DCM.M     % generative model (DCM)
% DCM.Ep    % Conditional means (structure)
% DCM.Cp    % Conditional covariances
% DCM.F     % (negative) Free-energy bound on log evidence
% 

    
function [DCM] = spm_dcm_mdp(DCM)
% MDP inversion using Variational Bayes
% FORMAT [DCM] = spm_dcm_mdp(DCM)
%
% Expects:
%--------------------------------------------------------------------------
% DCM.MDP   % MDP structure specifying a generative model
% DCM.field % parameter (field) names to optimise
% DCM.U     % cell array of outcomes (stimuli)
% DCM.Y     % cell array of responses (action)
%
% Returns:
%--------------------------------------------------------------------------
% DCM.M     % generative model (DCM)
% DCM.Ep    % Conditional means (structure)
% DCM.Cp    % Conditional covariances
% DCM.F     % (negative) Free-energy bound on log evidence
% 
% This routine inverts (cell arrays of) trials specified in terms of the
% stimuli or outcomes and subsequent choices or responses. It first
% computes the prior expectations (and covariances) of the free parameters
% specified by DCM.field. These parameters are log scaling parameters that
% are applied to the fields of DCM.MDP. 
%
% If there is no learning implicit in multi-trial games, only unique trials
% (as specified by the stimuli), are used to generate (subjective)
% posteriors over choice or action. Otherwise, all trials are used in the
% order specified. The ensuing posterior probabilities over choices are
% used with the specified choices or actions to evaluate their log
% probability. This is used to optimise the MDP (hyper) parameters in
% DCM.field using variational Laplace (with numerical evaluation of the
% curvature).
%
%__________________________________________________________________________
% Copyright (C) 2005 Wellcome Trust Centre for Neuroimaging

% Karl Friston
% $Id: spm_dcm_mdp.m 7120 2017-06-20 11:30:30Z spm $

% OPTIONS
%--------------------------------------------------------------------------
ALL = false;

% set default for precision: (inference – beta and action - alpha)
%--------------------------------------------------------------------------
if ~isfield(DCM.MDP,'alpha'), DCM.MDP.alpha = 4; end