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This work introduces the IB-score, a family of independence-based scorefunctions for robust learning of Markov networks independence structures.Markov networks are a widely used graphical representation of probabilitydistributions, with many applications in several fields of science. The mainadvantage of the IB-score is the possibility of computing it without the needof estimation of the numerical parameters, an NP-hard problem, usually solvedthrough an approximate, data-intensive, iterative optimization. We derive aformal expression for the IB-score from first principles, mainly maximum aposteriori and conditional independence properties, and exemplify severalinstantiations of it, resulting in two novel algorithms for structure learning:IBMAP-HC and IBMAP-TS. Experimental results over both artificial and real worlddata show these algorithms achieve important error reductions in the learntstructures when compared with the state-of-the-art independence-based structurelearning algorithm GSMN, achieving increments of more than 50% in the amount ofindependencies they encode correctly, and in some cases, learning correctlyover 90% of the edges that GSMN learnt incorrectly. Theoretical analysis showsIBMAP-HC proceeds efficiently, achieving these improvements in a timepolynomial to the number of random variables in the domain.

Efficient Independence-Based MAP Approach for Robust Markov Networks Structure Discovery