I understand the modular nature of directed models, and that each node captures a conditional probability. But why do we need undirected models?
As far as I can see they lack intuition in that the factors don't represent any type (conditional/marginal) of probability. Further, a final step of normalization is needed to convert the un-normalized measure into a true probability. So,
a) what could be the motivation behind introducing Markov nets at all.
b) what is a god intuition to understand factors, and their advantages as compared to the CPD's of Bayes nets.