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: https://www.youtube.com/watch?v=E8rOVwKQ5-8

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: "The Five Tribes of Machine Learning (And What You Can Learn from Each)," Pedro Domingos