title: Unsupervised Naive Bayes - how does it work?
body: <p>So as I understand it, to implement an unsupervised Naive Bayes, we assign random probability to each class for each instance, then run it through the normal Naive Bayes algorithm. I understand that, through each iteration, the random estimates get better, but I can't for the life of me figure out exactly how that works.</p>
  
  <p>Anyone care to shed some light on the matter?</p>