Consider jointly Gaussian random variables whose conditional independence structure is specified by a graphical model. If we observe realizations of the variables, we can compute the covariance matrix, and it is well known that the support of the inverse covariance matrix corresponds to the edges of the graphical model. Instead, suppose we only have noisy observations. If the noise at each node is independent, we can compute the sum of the covariance matrix and an unknown diagonal. The inverse of this sum is (in general) dense. We ask: can the original independence structure be recovered? We address this question for tree structured graphical models. We prove that this problem is unidentifiable, but show that this unidentifiability is limited to a small class of candidate trees. We further present additional constraints under which the problem is identifiable. Finally, we provide an O(n^3) algorithm to find this equivalence class of trees.
Ashish Katiyar (The University of Texas at Austin)
Jessica Hoffmann (University of Texas at Austin)
Constantine Caramanis (University of Texas)
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2019 Poster: Robust Estimation of Tree Structured Gaussian Graphical Models »
Thu Jun 13th 06:30 -- 09:00 PM Room Pacific Ballroom