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: demo_groupbtw.m
    path of file: ./repos/VBA-toolbox/demos/2_statistics
    
    the code of the file until where you have to start completion: % This demo evaluates group-level BMS when asking whether different
% conditions induce different models. More precisely, the code uses MCMC
% simulations to obtain distribution of Eps, BORs and protected EPs, given
% fixed frequency profiles of tuple families f= and f~=.
% See function VBA_groupBMCbtw.m

clear all
close all

Ndummy = 128;
Nmcmc = 16;
N = 32;
r0 = [0.5;0.5]; % model frequencies under the null 
dr = [-0.5:0.05:0.5]; % change in model frequencies
ep = zeros(Nmcmc,length(dr)); % exceedance prob (f=)
pep = zeros(Nmcmc,length(dr)); % protected exceedance prob (f=)
bor = zeros(Nmcmc,length(dr)); % bayesian omnibus risk

% create dummy log-evidences
L0(:,:,1) = [ones(1,Ndummy);-ones(1,Ndummy)];
L0(:,:,2) = -L0(:,:,1);
L0 = L0 + 1e-1*randn(size(L0));

for j=1:length(dr)
    j./length(dr)
    for ii=1:Nmcmc
        % sample 2-tuple families (
    
function VBA_groupBMCbtw.m

clear all
close all

Ndummy = 128;
Nmcmc = 16;
N = 32;
r0 = [0.5;0.5]; % model frequencies under the null 
dr = [-0.5:0.05:0.5]; % change in model frequencies
ep = zeros(Nmcmc,length(dr)); % exceedance prob (f=)
pep = zeros(Nmcmc,length(dr)); % protected exceedance prob (f=)
bor = zeros(Nmcmc,length(dr)); % bayesian omnibus risk

% create dummy log-evidences
L0(:,:,1) = [ones(1,Ndummy);-ones(1,Ndummy)];
L0(:,:,2) = -L0(:,:,1);
L0 = L0 + 1e-1*randn(size(L0));

for j=1:length(dr)
    j./length(dr)
    for ii=1:Nmcmc
        % sample 2-tuple families (homogeneous vs heterogeneous conditions)
        f = VBA_random ('Categorical', r0 + [dr(j); -dr(j)], N, 1);
        
        % sample model evidences for each model, subject and condition
        L = zeros(2,N,2); % 2xNx2 (2 models; N subjects; 2 conditions).
        for i=1:N
            i1 = floor(1+rand*(Ndummy-1));
            i2 = floor(1+rand*(Ndummy-1));
            % pick a model at random
            m = VBA_random ('Categorical', [0.5; 0.5]);
            
            % assign LEVs, given the subject's class
            if f(i)==1 % homogeneous condition
                L(:,i,1) = L0(:,i1,m);
                L(:,i,2) = L0(:,i2,m);
            else % heterogeneous condition
                L(:,i,1) = L0(:,i1,m);
                L(:,i,2) = L0(:,i2,3-m);
            end