Distributions and Conditionally Independent Features (I)
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<li>Let each <span class="math inline">\(\mathbf{x}_n\overset{\text{iid}}{\sim} \frac{1}{2}F_1 + \frac{1}{2}F_2\)</span>, where the two source distributions contributing to the mixture are given by <span class="math inline">\(F_1 = \prod_{i=1}^4 N(i^2, 1)\)</span> and <span class="math inline">\(F_2 =\prod_{i=1}^4 N(1/i^2, 1)\)</span>. The <span class="math inline">\(\Pi\)</span> notation here means that the <span class="math inline">\(P=4\)</spa