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.
Papers
Learning Bayesian Networks in the Presence of Structural Side Information
Ehsan Mokhtarian, Sina Akbari, Fateme Jamshidi, Jalal Etesami, Negar Kiyavash
Hybrid Bayesian network discovery with latent variables by scoring multiple interventions
Kiattikun Chobtham, Anthony C. Constantinou, Neville K. Kitson
High-Dimensional Inference in Bayesian Networks
Fritz M. Bayer, Giusi Moffa, Niko Beerenwinkel, Jack Kuipers
The Dual PC Algorithm and the Role of Gaussianity for Structure Learning of Bayesian Networks
Enrico Giudice, Jack Kuipers, Giusi Moffa
Forecasting sales with Bayesian networks: a case study of a supermarket product in the presence of promotions
Muhammad Hamza, Mahdi Abolghasemi, Abraham Oshni Alvandi