Bidyuk P.I.

Construction of Information-Analytical Platform for Scenario Analysis Based on Large Volumes of Weakly Structured Data

The research was aimed at creating an information-analytical platform that provides the conduct of scenario analysis for social, economic, ecological processes in complex systems with human factor, using deep analysis methods of large volumes of weakly structured data, aggregated from different sources, and expert approaches of

The development of information technology for modeling and evaluating the financial and economic risks with accounting of the uncertainties of different nature (based on Bayesian models)

A new technique of the data mining was proposed that combines the causal networks and methods for risk assessment in the form of stochastic volatility models. The approach includes the following stages: (1) definition and classification process critical elements under study, in order to identify and characterize risk factors; (2) constructing causal model in the form of believe Bayesian networks; (3) create a set of candidates scenarios process; (4) modeling and evaluating the risks of the critical factors based on Bayesian stochastic volatility models using methods of optimal filtering.

Development and implementation of methodology for intellectual data analysis using Bayesian networks theory and regression analysis

A new two-stage method for intellectual data analysis is proposed that combines Bayesian networks theory and regression analysis. The method is based on two sets of mathematical techniques. The first one is used for constructing topology of Bayesian network and forming probabilistic inference. The inference is used further on for decision making on the basis of forecast estimates. The second set of methods is used for development of regression model with making use of logistic link function that serves as a basis for forecast estimation.

Construction decision support system by using Bayesian network’s theory for modeling behavior of complex systems

Developed some of methods for solving ill-structed problems for modeling, prediction and classification. All methods use Bayesian networks. Proposed a new five step method for finding the parameters of Bayesian networks with hidden nodes. Method bases on an expectation maximization algorithm. Suggested Pearson's, Chuprov's, Cramer's, Goodman's and mutual information coefficients for finding interconnections between Bayesian network's nodes. For solving the problem of modeling the behavior of complex systems proposed original method for construction and application hybrid Bayesian networks.