Bayes Net

Bayesian networks (Bayes nets) are probabilistic graphical models used to represent and reason with uncertain information, aiming to efficiently capture dependencies between variables. Current research focuses on improving scalability and efficiency for large datasets, particularly through variational inference and novel architectures like Bayesian neural networks and neural fields, often incorporating techniques such as Monte Carlo dropout and LoRA for parameter reduction and improved performance. These advancements are driving applications in diverse fields, including search systems, medical data imputation, intelligent tutoring systems, and safety-critical applications requiring reliable uncertainty quantification.