Principles and analytical tools for reconstruction of probabilistic dependency structures in special class
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 depende...
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| Date: | 2018 |
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| Main Author: | |
| Format: | Article |
| Language: | Ukrainian |
| Published: |
PROBLEMS IN PROGRAMMING
2018
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| Subjects: | |
| Online Access: | https://pp.isofts.kiev.ua/index.php/ojs1/article/view/225 |
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| Journal Title: | Problems in programming |
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Problems in programming| Summary: | 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 |
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