What is easy and what is hard in a DAG?
It is easy to generate an
unbiased example at the leaf
It is typically hard to compute
the posterior distribution over
all possible configurations of
hidden causes. It is also hard
to compute the probability of
an observed vector.
Given samples from the
posterior, it is easy to learn the
conditional probabilities that
define the model.
Hidden cause