RT Journal Article ID 2fa04e0106f83870 A1 Balabanov, Alexander S. T1 Reconstruction of the Model of Probabilistic Dependences by Statistical Data. Tools and Algorithm JF Journal of Automation and Information Sciences JO JAI(S) YR 2009 FD 2009-12-30 VO 41 IS 12 SP 32 OP 46 K1 probabilistic dependences K1 statistical data model K1 reconstruction K1 Bayesian networks K1 tools K1 algorithm AB The tools and an algorithm for reconstruction of probability models of dependencies in the class of monoflow structures (a subclass of Bayesian networks), are developed. "Proliferator-D" algorithm is computationally efficient (subcubic complexity) and performs a small number of tests of conditional independence only of the first rank. The correctness of the algorithm is justified by simple assumptions, which are empirically robust with respect to the size of a data sample. When the generative model goes beyond monoflow structures, the algorithm gradually degrades to the known Kruskal algorithm and produces the cover (approximation) of the model by a tree. The proposed algorithm can be easily modified to improve the quality of reduction (approximation) of Bayesian networks. PB Begell House LK https://www.dl.begellhouse.com/journals/2b6239406278e43e,572fa2f44f5597b8,2fa04e0106f83870.html