Are there complexity theoretic results about recoverability or learnability of the marginals (of the source vertices) and the conditionals (along each of the edges) of a Bayesian network from having access to some subset of the vertices?
Are there at least known conditions when this is provably impossible or when some hardness result has been proven?
I am typically assuming that the random variables (one at each vertex) are mapping the same sample space into some finite space which could be different for each random variable/vertex. I would equally well want to know if there are results with continuous distributions.
I would be equally well helped to know if there are such results known with Marokov Random Fields or Factor Graphs.