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自动化与信息科学期刊
SJR: 0.275 SNIP: 0.59 CiteScore™: 0.8

ISSN 打印: 1064-2315
ISSN 在线: 2163-9337

卷:
卷 52, 2020 卷 51, 2019 卷 50, 2018 卷 49, 2017 卷 48, 2016 卷 47, 2015 卷 46, 2014 卷 45, 2013 卷 44, 2012 卷 43, 2011 卷 42, 2010 卷 41, 2009 卷 40, 2008 卷 39, 2007 卷 38, 2006 卷 37, 2005 卷 36, 2004 卷 35, 2003 卷 34, 2002 卷 33, 2001 卷 32, 2000 卷 31, 1999 卷 30, 1998 卷 29, 1997 卷 28, 1996

自动化与信息科学期刊

DOI: 10.1615/JAutomatInfScien.v41.i12.20
pages 32-46

Reconstruction of the Model of Probabilistic Dependences by Statistical Data. Tools and Algorithm

Alexander S. Balabanov
Institute of Software Systems of National Academy of Sciences of Ukraine, Kyiv, Ukraine

ABSTRACT

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.


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