codigo.m

%% Prior and Posterior Predictive, Second Example
clear;

%% Data
k = zeros(4,3);
k(1,:) = [0 0 0];
k(2,:) = [1 0 1];
k(3,:) = [1 4 6];
k(4,:) = [2 3 6];

n = [2 4 6]

%% Sampling
% MCMC Parameters
nchains = 2; % How Many Chains?
nburnin = 1e2; % How Many Burn-in Samples?
nsamples = 1e4;  %How Many Recorded Samples?
nthin = 1; % How Often is a Sample Recorded?
doparallel = 0; % Parallel Option

% Assign Matlab Variables to the Observed Nodes
datastruct = struct('k',k,'n',n);

% Initialize Unobserved Variables
for i=1:nchains
    S.theta = [0.5 0.5 0.5 0.5]; % Intial Value
    S.alpha = 1;
    init0(i) = S;
end

    % Use JAGS to Sample
    tic
    fprintf( 'Running JAGS ...\n' );
    [samples, stats] = matjags( ...
        datastruct, ...
        fullfile(pwd, 'modelo.txt'), ...
        init0, ...
        'doparallel' , doparallel, ...
        'nchains', nchains,...
        'nburnin', nburnin,...
        'nsamples', nsamples, ...
        'thin', nthin, ...
        'monitorparams', {'theta','alpha'}, ...
        'savejagsoutput' , 1 , ...
        'verbosity' , 1 , ...
        'cleanup' , 0 , ...
        'workingdir' , 'tmpjags' );
    toc

% Plot Posterior
stats.mean.theta
stats.mean.alpha

model.txt

model {
  alpha ~ dunif(0.01,3) 
  for (i in 1:4){
    theta[i] ~ dbeta(alpha,alpha)
    for (j in 1:3){
     k[i,j] ~ dbin(theta[i],n[j])
    }
  }   
}