Abstract
This study investigates evidence propagation in Bayesian networks with complex topologies, aiming to improve inference efficiency beyond the limitations of traditional singly connected (polytree) structures. An iterative inference algorithm is developed that leverages structural properties such as d-separation and the Markov blanket. The proposed method partitions networks into modular components and iteratively propagates evidence through them. The algorithm is tested on Bayesian networks with multiply connected graphs, using both forward and backward propagation phases to ensure convergence. The findings demonstrate that the method significantly reduces computational complexity while maintaining high accuracy. By localizing computations and employing iterative updates, the algorithm achieves efficient convergence even in the presence of multiple cycles and conflicting pieces of evidence. Experimental results confirm the robustness of the proposed approach. The iterative propagation algorithm enhances the applicability of Bayesian networks to real-world scenarios involving high-dimensional and interconnected variables, overcoming the limitations of standard methods and enabling scalable, accurate probabilistic reasoning. The algorithm has practical implications for decision support systems, medical diagnostics, and intelligent data processing, where real-time inference in complex network structures is essential.