Gaussian Bayesian Network

Gaussian Bayesian Networks (GBNs) are probabilistic graphical models used to represent and reason with uncertainty in systems where variables follow Gaussian distributions. Current research focuses on improving the efficiency of algorithms for tasks like entropy and Kullback-Leibler divergence calculations, as well as developing methods for learning GBN structures from heterogeneous datasets, leveraging techniques like mixed-effects models. GBNs find applications in diverse fields, including risk prediction in transportation systems and reliability analysis of complex systems, offering advantages in explainability and handling high-dimensional data. Their use in deep learning models, such as "rainbow networks," is also an emerging area of investigation.