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Jiming Liu, Michel C. Desmarais, "A Method of Learning Implication Networks from Empirical Data: Algorithm and MonteCarlo SimulationBased Validation," IEEE Transactions on Knowledge and Data Engineering, vol. 9, no. 6, pp. 9901004, NovemberDecember, 1997.  
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@article{ 10.1109/69.649321, author = {Jiming Liu and Michel C. Desmarais}, title = {A Method of Learning Implication Networks from Empirical Data: Algorithm and MonteCarlo SimulationBased Validation}, journal ={IEEE Transactions on Knowledge and Data Engineering}, volume = {9}, number = {6}, issn = {10414347}, year = {1997}, pages = {9901004}, doi = {http://doi.ieeecomputersociety.org/10.1109/69.649321}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
RefWorks Procite/RefMan/Endnote  x  
TY  JOUR JO  IEEE Transactions on Knowledge and Data Engineering TI  A Method of Learning Implication Networks from Empirical Data: Algorithm and MonteCarlo SimulationBased Validation IS  6 SN  10414347 SP990 EP1004 EPD  9901004 A1  Jiming Liu, A1  Michel C. Desmarais, PY  1997 KW  Beliefnetwork induction KW  probabilistic reasoning KW  learning algorithms KW  evidential reasoning KW  implication networks KW  implicationnetwork induction KW  knowledge engineering KW  MonteCarlo simulation KW  empirical validation. VL  9 JA  IEEE Transactions on Knowledge and Data Engineering ER   
Abstract—This paper describes an algorithmic means for inducing implication networks from empirical data samples. 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 probablistic network requirements are relaxed in the construction of dependence relationships based on statistical testing. In order to examine the effectiveness and validity of the induction method, several MonteCarlo simulations were conducted, where theoretical Bayesian networks were used to generate empirical data samples—some of which were used to induce implication relations, whereas others were used to verify the results of evidential reasoning with the induced networks. The values in the implication networks were predicted by applying a modified version of the DempsterShafer belief updating scheme. The results of predictions were, furthermore, compared to the ones generated by Pearl's stochastic simulation method [21], a probabilistic reasoning method that operates directly on the theoretical Bayesian networks. The comparisons consistently show that the results of predictions based on the induced networks would be
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