Are Bayesian approaches (static and dynamic) used for classification (which is supervised) or for clustering (which is unsupervised)? or can they be used for both ?
I even see that for instance to compute the likelihood they need the class labels of data, so I was thinking that it is only convenient for supervised cases where we have the class labels
Best Answer
Bayesian methods are very general. While there obviously is Naive Bayes, it can of course be used outside the "labeled data" domains.
Bayesian statistic is often defined on sets. The sets could be labels, but they could also be anything else. I figure you could use Bayesian statistics to test for mutual information, for example. So this common use of statistics - sets or predicates, and probably generalizable to fuzzy sets - can be used in various disciplines. After all, I can define predicates such as $x_3 < 7$ and then talk about the probability $P(x_5 > 3 | x_3 < 7)$ depending on a prior of $P(x_3 < 7)$ etc.