Bayesian Networks as a Tool for Modeling Complex Natural Systems with a High Level of Indeterminacy

At the present stage of the development of science, scientists are engaged in the study of big complex systems that are poorly structured, have a high level of uncertainty and unpredictability of the course of various processes, and are complicated by the presence of risk factors in these systems for the appearance of unforeseen situations, which allows them to be attributed to the class of complex tasks of dynamic process analysis random nature. Classic well-studied methods of modeling and forecasting do not give reliable results, which forces us to look for new methods of analysis and research of complex systems. An example of high-performance forecasting methods is Bayesian networks based on expert judgment and a priori and a posteriori observational data. Bayesian confidence networks make it possible to make a probabilistic forecast of both the resulting indicators and shows possible alternatives under the influence of some control factors. They also allow you to automatically build chains of dependencies between factors of different levels, which are based on the results of multiple observations. Bayesian networks are used in various fields of research, including: in medicine, meteorology, and the study of natural disasters.