))
ylabel(sprintf(<span class="string">'%s'</span>, <span class="string">'\mu_2'</span>))
title(<span class="string">'posterior on \mu'</span>)
printPmtkFigure(<span class="string">'gibbsGaussParamsPostMu'</span>)

quantilePMTK( samples.mu(2,:) - samples.mu(1,:), [0.025 0.5 0.975])
mean(samples.mu(2,:) &gt; samples.mu(1,:))


<span class="comment">% Compute posterior predictive</span>
Xpred = zeros(2,Nsamples);
<span class="keyword">for</span> i=1:Nsamples
  x = gaussSample(samples.mu(:,i), samples.Sigma(:,