[0 100], [0 100], 'color', 'r', 'linewidth', 3);
%set(gca, 'xlim', [0 100]);
%set(gca, 'ylim', [0 100]);
xlabel(sprintf('%s', 'x_1'))
ylabel(sprintf('%s', 'x_2'))
title('posterior predictive')
printPmtkFigure('gibbsGaussParamsPostPred')

end

function [samples] = gibbsSampler(data, params, nSamples)
% Sample mu and Sigma given a fully observed MVN
% samples.mu(:,s), samples.Sigma(:,:,s)
% We assume a factored Normal * IW prior
% See Peter Hoff's book sec 7.4
% 
% Written by Emtiyaz, CS, UBC 
% Modified on J