Method of learning implication networks from empirical data: algorithm and Monte-Carlo simulation-based validation

An algorithmic means for inducing implication networks from empirical data samples are described. The induced network enables efficient inferences about the values of network nodes if certain observations are made. This implication induction method is approximate in nature as probabilistic network requirements are relaxed in the construction of dependence relationships based on statistical testing. The effectiveness and validity of the induction method are examined by conducting Monte Carlo simulations. The values in the implication networks are also predicted by applying a modified version of the Dempster-shafer belief updating scheme.