Bayesian Network

Bayesian networks are probabilistic graphical models used to represent and reason with uncertain information, primarily aiming to model complex relationships between variables and make predictions under uncertainty. Current research focuses on improving the scalability of Bayesian network learning algorithms for high-dimensional data, developing efficient inference methods, and integrating them with other machine learning techniques like deep learning and large language models for enhanced performance and interpretability. These advancements are impacting diverse fields, including healthcare (disease modeling, diagnosis), manufacturing (process optimization), and autonomous systems (scenario planning, anomaly detection), by providing robust and explainable models for decision-making in complex systems.