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_bmc_peb.m
    path of file: ./repos/spm
    
    the code of the file until where you have to start completion: function [BMC,PEB] = spm_dcm_bmc_peb(DCM,M,field)
% Hierarchical (PEB) model comparison and averaging (1st and 2nd level)
% FORMAT [BMC,PEB] = spm_dcm_bmc_peb(DCM,[M,field])
%
% DCM    - {N [x M]} structure array of DCMs from N subjects
% ------------------------------------------------------------
%     DCM{i}.M.pE - prior expectation of parameters
%     DCM{i}.M.pC - prior covariances of parameters
%     DCM{i}.Ep   - posterior expectations
%     DCM{i}.Cp   - posterior covariance

    
function [BMC,PEB] = spm_dcm_bmc_peb(DCM,M,field)
% Hierarchical (PEB) model comparison and averaging (1st and 2nd level)
% FORMAT [BMC,PEB] = spm_dcm_bmc_peb(DCM,[M,field])
%
% DCM    - {N [x M]} structure array of DCMs from N subjects
% ------------------------------------------------------------
%     DCM{i}.M.pE - prior expectation of parameters
%     DCM{i}.M.pC - prior covariances of parameters
%     DCM{i}.Ep   - posterior expectations
%     DCM{i}.Cp   - posterior covariance
%
% M.X    - second level design matrix, where X(:,1) = ones(N,1) [default]
% M.pE   - second level prior expectation of parameters
% M.pC   - second level prior covariances of parameters
% M.hE   - second level prior expectation of log precisions
% M.hC   - second level prior covariances of log precisions
%
% field  - parameter fields in DCM{i}.Ep to optimise [default: {'A','B'}]
%          'All' will invoke all fields (i.e. random effects)
%
% BMC    - Bayesian model comparison structure 
% -------------------------------------------------------------
%     BMC.F    - free energy over joint model space
%     BMC.P    - posterior probability over models
%     BMC.Px   - posterior probability over 1st level models
%     BMC.Pw   - posterior probability over 2nd level models
%     BMC.M    - second level model
%     BMC.K    - model space
%
% PEB    - selected (best) second level model and parameter estimates
% -------------------------------------------------------------
%     PEB.Snames - string array of first level model names
%     PEB.Pnames - string array of parameters of interest
%     PEB.Pind   - indices of parameters in spm_vec(DCM{i}.Ep) 
%
%     PEB.M    -   first level (within subject) model
%     PEB.Ep   -   posterior expectation of second level parameters
%     PEB.Eh   -   posterior expectation of second level log-precisions
%     PEB.Cp   -   posterior covariance  of second level parameters
%     PEB.Ch   -   posterior covariance  of second level log-precisions
%     PEB.Ce   -   expected covariance of second level random effects
%     PEB.F    -   free energy of second level model
%
%--------------------------------------------------------------------------
% This routine performs Bayesian model comparison in the joint space of
% models specified in terms of (first level) model parameters and models
% specified in terms of (second level) group effects. The first level model
% space is defined by the columns of the DCM array, while the second level
% model space is specified by combinations of second level effects encoded 
% in a design matrix.  The first effect in the design matrix is assumed 
% to be a constant term that models a group mean.
%
% This routine assumes that all the models have been reduced (i.e. inverted
% using Bayesian model reduction). It then use sempirical Bayes and the
% summary statistic approach to evaluate the relative contributions of
% between subject effects by considering all combinations of columns in the
% design matrix.
%
% This Bayesian model comparison should be contrasted with model
% comparison at the second level. Here, we are interested in the best model
% of first level parameters that show a second level effect. This is not
% the same as trying to find the best model of second level effects. Model
% comparison among second level parameters uses spm_dcm_peb_bmc.
%
% NB for EEG models the absence of a connection means it is equal to its
% prior mesn, not that is is zero.
%
% see also: spm_dcm_peb.m and spm_dcm_bmr_peb
%__________________________________________________________________________

% Karl Friston
% Copyright (C) 2015-2022 Wellcome Centre for Human Neuroimaging


% set up
%==========================================================================

% Number of subjects (data) and models (of those data)
%--------------------------------------------------------------------------
[Ns,Nm]   = size(DCM);

% % second level model
%--------------------------------------------------------------------------
if nargin < 2
    M.X   = ones(Ns,1);
end