Abstract
A review of some Bayesian data analysis models is proposed, namely the models with one and several parameters. A methodology is developed for probabilistic models construction in the form of Bayesian networks using statistical data and expert estimates. The methodology provides a possibility for constructing high adequacy probabilistic models for solving the problems of classification and forecasting. An integrated dynamic network model is proposed that is based on combination of probabilistic and regression approaches; the model is distinguished with a possibility for multistep forecasts estimation. The forecast estimates computed with the dynamic model are compared with the results achieved with logistic regression combined with multiple regression. The best results were achieved in this case with the combined dynamic net model.Â
Highlights
The modern information technologies and statistical analysis of experimental data is based on usage of a wide variety of methods that are characterized by the in-depth and comprehensive analysis of available measurements and development of mathematical and statistical models of high degree of adequacy
An overview of some Bayesian models to analyze the data aiming to determine the possibility of their use for predicting development of processes of arbitrary nature was presented
The methodology is proposed for constructing probabilistic models in the form of Bayesian networks that is based based on statistical data and expert estimates
Summary
The modern information technologies and statistical analysis of experimental data is based on usage of a wide variety of methods that are characterized by the in-depth and comprehensive analysis of available measurements and development of mathematical and statistical models of high degree of adequacy. Disadvantages include the need for generating a large number of rules when studying multidimensional processes and the impossibility of tracking their use by a decision making person (DMP) while forming the inference Another broad class of modeling methods for prediction and management, which are aimed at treating uncertainty, is based on the Bayesian approach to data analysis [8 – 12]. In view of the needs to improve the methods of fighting uncertainties of various types and the availability of a wide range of methods for Bayesian data analysis, it is necessary to know their application possibilities, advantages and disadvantages This is important in the context of decision support system (DSS) development [13 – 15] as far as probabilistic methods and models make it possible to obtain important additional alternative methods for decision making based on regression analysis, fuzzy logic, neural networks, and so on. To some extent this problem is partially solved in this paper through examination of some popular methods of Bayesian data analysis and probabilistic models, and an attempt is made for association with other types of models, including regression
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