Bayesian Network Structure
Bayesian network structure learning aims to discover the optimal graphical representation of probabilistic relationships between variables, facilitating causal inference and prediction. Current research emphasizes developing efficient algorithms, including those based on score-equivalence criteria, hierarchical computation, and parallel processing, to overcome the computational complexity of learning structures from high-dimensional data. These advancements are improving the accuracy and scalability of Bayesian network construction, with applications ranging from biomedical research (e.g., diabetes biomarker analysis, sepsis risk prediction) to engineering (e.g., rail transit risk assessment) and public health (e.g., COVID-19 symptom analysis). The field is also actively exploring methods to incorporate prior knowledge and address challenges like variable ordering and sparsity assumptions to enhance the reliability and interpretability of learned structures.