Modeling technology based on fuzzy object-oriented Bayesian belief networks

S.V. Yershov, F.V. Kostukevich

Abstract


The basic components of information technology inductive modeling causation under uncertainty based on fuzzy object-oriented Bayesian networks is proposed. The technology is based on a combination of transformation algorithms Bayesian network in the junction tree. New more efficient algorithms for Bayesian network transformation are resulted from modifications known algorithms; algorithms based on the use of more information on the graphical representation of the network are considered. Structurally functional model are described, it is designed to implement the transformation of fuzzy object-oriented Bayesian networks.

Problems in programming 2016; 2-3: 179-187


Keywords


Bayesian network; object-oriented Bayesian network; fuzzy Bayesian network; transformation algorithms; moral graph; junction tree; fuzzy probability

Full Text:

PDF (Ukrainian)

References


Сowell R.G., Dawid A.P., Spiegelhalter D.J., Lauritzen S .L. (1999) Probabilistic Networks and Expert Systems. – Springer–Verlag, New York, Inc.

Uffe B. Kjaerulff, Anders L. Madsen (2008) Bayesian Networks and Influence Diagrams. – Springer Science+Business Media, LLC

Lotfy Zade (1974) Problem is based on a new approach to the analysis of complex systems and decision-making processes // Mathematics Today (collection of articles translated from English.). M. "Knowledge".

Verovka O.V., Parasjuk I .N. (2008) On the propagation of probabilities in Bayesian networks with fuzzy non-deterministic states // Cybernetics and Systems Analysis, №6.

Parasyuk I.M., Kostukevich F.V. (2008) The methods of transform Bayesian network methods to construct a tree of nodes and their modification // Computer mathematics, №1, K .: Institute of Cybernetics Glushkov National Academy of Sciences of Ukraine.

Parasyuk I.M., Kostukevich F.V. (2009) Fuzzy potentials and problems of their use in algorithms spread confidence in the Bayesian networks // Computer mathematics, №1.- K. : Institute of Cybernetics. Glushkov National Academy of Sciences of Ukraine.

Parasyuk I.M., Kostukevich F.V. (2010) An efficient algorithm of fuzzy probability distribution in Bayesian networks of trust / / Computer mathematics, №2.- K .: Institute of Cybernetics. Glushkov. National Academy of Sciences of Ukraine.

Pearl J. (1991) Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. – Morgan Kaufmann, San Mateo, CA.

Steffen L. Lauritzen, David J. Spiegelhalter (1988) Local Computations with Probabilities on Graphical Structures and Their Application to Expert Systems // Journal of the Royal Statistical Society, Series B, Vol. 50, No. 2.

Pinar Heggernes (2006) Minimal triangulations of graphs: A survey// Discrete Mathematics, Volume 306, Issue 3. – Department of Informatics, University of Bergen, N-5020 Bergen, Norway

Shenoy P.P. and Shafer G. (1990) Axioms for probability and belief-function propagation. // In Uncertainty in Artificial Intelligence 4

F. Jensen, S. Lauritzen, and K. Oesen (1990) Bayesian updating in causal probabilistic networks by local computations // SIAM Journal on Computing, 4.

V. Lepar and P. Shenoy. (1998) A comparison of Lauritzen-Spiegelhalter, Hugin and Shenoy-Shafer architectures for computing marginals of probability distributions. // In G. Cooper and S. Moral, editors, Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence (UAI-98), Morgan Kaufmann


Refbacks

  • There are currently no refbacks.