Learning to Sample from Censored Markov Random Fields
This addresses a fundamental challenge in statistical learning for partially observed graphical models, with potential applications in domains like network analysis or sensor data, but it appears incremental as it builds on existing MRF learning methods.
The authors tackled the problem of learning Censored Markov Random Fields (CMRFs) where some nodes are unobserved, presenting an algorithm that achieves o(n) transportation distance for high-temperature CMRFs without assumptions on graph structure or observed nodes, with stronger results for high girth cases and computational lower bounds showing the results are optimal.
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.