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In this work we consider the problem of learning the structure of Markovnetworks from data. We present an approach for tackling this problem calledIBMAP, together with an efficient instantiation of the approach: the IBMAP-HCalgorithm, designed for avoiding important limitations of existingindependence-based algorithms. These algorithms proceed by performingstatistical independence tests on data, trusting completely the outcome of eachtest. In practice tests may be incorrect, resulting in potential cascadingerrors and the consequent reduction in the quality of the structures learned.IBMAP contemplates this uncertainty in the outcome of the tests through aprobabilistic maximum-a-posteriori approach. The approach is instantiated inthe IBMAP-HC algorithm, a structure selection strategy that performs apolynomial heuristic local search in the space of possible structures. Wepresent an extensive empirical evaluation on synthetic and real data, showingthat our algorithm outperforms significantly the current independence-basedalgorithms, in terms of data efficiency and quality of learned structures, withequivalent computational complexities. We also show the performance of IBMAP-HCin a real-world application of knowledge discovery: EDAs, which areevolutionary algorithms that use structure learning on each generation formodeling the distribution of populations. The experiments show that whenIBMAP-HC is used to learn the structure, EDAs improve the convergence to theoptimum.

The IBMAP approach for Markov networks structure learning