Author :
Mina Karzand;Guy Bresler
Author_Institution :
Department of Electrical Engineering and Computer Science at MIT, United States
Abstract :
We consider the problem of learning an Ising model for the purpose of subsequently performing inference from partial observations. This is in contrast to most other work on graphical model learning, which tries to learn the true underlying graph. This objective requires a lower bound on the strength of edges for identifiability of the model. We show that in the relatively simple case of tree models, the Chow-Liu algorithm learns a distribution with accurate low-order marginals despite the model possibly being non-identifiable. In other words, a model that appears rather different from the truth nevertheless allows to carry out inference accurately.
Keywords :
"Computational modeling","Vegetation","Lead","Inference algorithms","Mutual information","Graphical models","Complexity theory"
Conference_Titel :
Communication, Control, and Computing (Allerton), 2015 53rd Annual Allerton Conference on
DOI :
10.1109/ALLERTON.2015.7447164