Which tribe does Probabilistic Graphical Models fall under? Pedro Domingos in  "The Master Algorithm" listed five tribes of machine learning algorithms:


Symbolists 
Connectionists 
Evolutionaries 
Bayesians
Analogizers 


Which category do probabilistic graphical models fall under?

From wikipedia (https://en.wikipedia.org/wiki/Graphical_model):


  A graphical model or probabilistic graphical model (PGM) is a
  probabilistic model for which a graph expresses the conditional
  dependence structure between random variables. They are commonly used
  in probability theory, statistics—particularly Bayesian statistics—and
  machine learning.


In that case, would it be "Bayesian"? What about something like Conditional Random Field? Is that Bayesian as well?
Probabilistic Graphical Models (PGMs) are:

Connectionist: RBMs are PGMs and neural networks (source)
Bayesian: Bayes Networks are bayesian (Wikipedia article)
Symbolist: Markov Logic Networks (source)
Analogizers and Evolutionaries: According to Domingos, they are also in Markov Logic Networks.

So the answer is that you can't simply categorize such a general technique as probabilistic graphical models in a single one of those categories.
See also: &quot;The Five Tribes of Machine Learning (And What You Can Learn from Each),&quot; Pedro Domingos