title: How to compute PCA "in closed form" in sklearn?
body: <p>It is said in <a href="http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.FactorAnalysis.html" rel="nofollow noreferrer">documentation</a>, that</p>
  <blockquote>
  <p>See also</p>
  <p>PCA Principal component analysis is also a latent linear
  variable model which however assumes equal noise variance for each
  feature. This extra assumption makes probabilistic PCA faster as it
  can be computed <strong>in closed form</strong>.</p>
  </blockquote>
  <p>What does it mean? How to take benefit of this form of PCA?</p>