2 citations · 2 across the 3 of their papers we have counts for
Showing cs.AIShow all
2 papers · 1 filter
cs.AI2024★ 1 cited
Entropy and the Kullback-Leibler Divergence for Bayesian Networks: Computational Complexity and Efficient Implementation
Marco Scutari
Bayesian networks (BNs) are a foundational model in machine learning and causal inference. Their graphical structure can handle high-dimensional problems, divide them into a sparse…
cs.AI2023★ 2 cited
Risk Assessment of Lymph Node Metastases in Endometrial Cancer Patients: A Causal Approach
Alessio Zanga, Alice Bernasconi, Peter J. F. Lucas +4
Assessing the pre-operative risk of lymph node metastases in endometrial cancer patients is a complex and challenging task. In principle, machine learning and deep learning models…