Principles and analytical tools for reconstruction of probabilistic dependency structures in special class

• Main Author: Balabanov, O.S.
• Format: Article
• Language: Ukrainian
• Published: Інститут програмних систем НАН України 2018
• Subjects: dependence structure recovery; mono-flow graph; unconditional dependence; empirical manifestation of dependence; empirical resolutions of edge identification; UDC 004.855:519.216
• Online Access: https://pp.isofts.kiev.ua/index.php/ojs1/article/view/225

We examine a problem of reconstruction of dependency structure from data. It is assumed that model structure belongs to class of "mono-flow" graphs, which is a subclass of acyclonic digraph (known as DAGs) and is super-class relatively to the poly-trees. Properties of the mono-flow dependency models are examined, especially in terms of patterns of unconditional dependencies and mutual information. We characterize the twin-association evolving among two variables. Specialized methods of inference of mono-flow dependency model are briefly reviewed. To justify correctness of model recovery from data we formulate an assumption of unconditional (marginal) edge-wise faithfulness, perhaps the most reliable one among all simple versions of Causal faithfulness assumption. On the basis of the assumption and the properties of mono-flow dependency models we derive several empirical resolutions for edge identification, which make use 2-placed statistics only. A lot of experiments with artificial data have demonstrated efficiency of the resolutions in that they correctly recover many edges and commit low error rate.Problems in programming 2017; 1: 97-110