Inscrição na biblioteca: Guest
Journal of Automation and Information Sciences

Publicou 12 edições por ano

ISSN Imprimir: 1064-2315

ISSN On-line: 2163-9337

SJR: 0.173 SNIP: 0.588 CiteScore™:: 2

Indexed in

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

Volume 41, Edição 12, 2009, pp. 32-46
DOI: 10.1615/JAutomatInfScien.v41.i12.20
Get accessGet access

RESUMO

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.

CITADO POR
  1. Balabanov O. S., Induced Dependence, Factor Interaction, and Discriminating Between Causal Structures, Cybernetics and Systems Analysis, 52, 1, 2016. Crossref

  2. Balabanov O.S., On the classes of causal networks, identifiable by simple independence tests, PROBLEMS IN PROGRAMMING, 2-3, 2018. Crossref

  3. Balabanov O.S., Principles and analytical tools for reconstruction of probabilistic dependency structures in special class, PROBLEMS IN PROGRAMMING, 1, 2017. Crossref

Portal Digital Begell Biblioteca digital da Begell eBooks Diários Referências e Anais Coleções de pesquisa Políticas de preços e assinaturas Begell House Contato Language English 中文 Русский Português German French Spain