Learning Factor Graphs in Polynomial Time & Sample Complexity

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We study computational and sample complexity of parameter and structure learning in graphical models.

• Our main result shows that the class of factor graphs with bounded factor size and bounded connectivity can be learned in polynomial time and polynomial number of samples, assuming that the data is generated by a network in this class.
• This result covers both parameter estimation for a known network structure and structure learning.
• It implies as a corollary that we can learn factor graphs for both Bayesian networks and Markov networks of bounded degree, in polynomial time and sample complexity.