n> features are generated independently according to unit variance Gaussian distributions.<br>
</li>
</ul>
</li>
<li>Mixture Distributions and Conditionally Independent Features (II)
<ul>
<li>Let <span class="math inline">\(\mathbf{x}_n\overset{\text{iid}}{\sim} \frac{1}{2}F_1 + \frac{1}{2}F_2\)</span>, but now <span class="math inline">\(F_1 = N(1,1) \times N(4,1) \times N(-4,1) \times N(-1,1)\)</span> and <span class="math inline">\(F_2 = N(-1,1) \times N(-4,1) \times N(4,1)\times N(1,1)\)</span>.</li>
</