  We study learning Censor Markov Random Fields (abbreviated CMRFs). These are
Markov Random Fields where some of the nodes are censored (not observed). We
present an algorithm for learning high-temperature CMRFs within o(n)
transportation distance. Crucially our algorithm makes no assumption about the
structure of the graph or the number or location of the observed nodes. We
obtain stronger results for high girth high-temperature CMRFs as well as
computational lower bounds indicating that our results can not be qualitatively
improved.
