CONCEPT OF ADAPTIVE SELECTION OF STRUCTURED LEARNING ALGORITHMS OF BAYESIAN NETWORKS BASED ON THEIR CHARACTERISTICS

Background. Modern intelligent systems require efficient mechanisms for analysis, prediction, and decision-making. Bayesian networks allow for the efficient representation of causal relationships between variables. The quality and reliability of the constructed network directly correlate with the effectiveness of the final intelligent system. Materials and Methods. Structural training of a Bayesian network involves determining the structure of a directed acyclic graph in which variables are related to each other. The quality of the structure has a decisive impact on the ability of the model to accurately represent conditional probabilities and on the efficiency of the training algorithms and the reliability of the model. The main problem limiting the structural learning of Bayesian networks is the computational complexity of the model. This fundamental complexity means that for multidimensional problems, it is impossible to perform a complete search of all possible structures and find a global optimum. This forces reliance on heuristic search methods and approximation algorithms and creates a constant need to balance the quality of structure determination and computational resources. Results and Discussion. The formalized concept of adaptive selection of algorithms for structural learning is based on a systematic analysis of algorithm characteristics and data properties, which allows you to choose the most suitable algorithm for a particular case, optimize the trade-off between model quality and computational resources, and increase the generalizability of the model in practical scenarios. Conclusion. The proposed concept of adaptive selection of algorithms for structural learning is a timely contribution to the field of stochastic dependence modeling. It successfully translates the process of selecting the optimal algorithm from a routine, heuristic, brute force method to a systematic, multivariate analysis. Its full implementation has the potential to significantly increase the reliability, accuracy, and computational efficiency of building Bayesian models in complex analytical domains.