Which graphical models are difficult to learn?

Details Which graphical models are difficult to learn? Which graphical models are difficult to learn?

2009-10-30

arxiv.org/abs/0910.5761v1

Statistics

Machine Learning

Scientific paper

We consider the problem of learning the structure of Ising models (pairwise binary Markov random fields) from i.i.d. samples. While several methods have been proposed to accomplish this task, their relative merits and limitations remain somewhat obscure. By analyzing a number of concrete examples, we show that low-complexity algorithms systematically fail when the Markov random field develops long-range correlations. More precisely, this phenomenon appears to be related to the Ising model phase transition (although it does not coincide with it).

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