Revisiting Bayesian Network Learning with Small Vertex Cover

Explore a conference talk from the Uncertainty in Artificial Intelligence (UAI) 2023 conference that delves into the complexities of Bayesian network structure learning. Investigate the potential for improving polynomial algorithms in DAGs with bounded vertex cover numbers and examine the challenges of Bayesian learning through sampling and weighted counting of DAGs. Learn about the #P-hardness proof for the general counting problem and the #W[1]-hardness of counting under vertex-cover constraints. Gain insights from the research of Juha Harviainen and Mikko Koivisto as they revisit and expand upon previous work in this field, offering new perspectives on the computational complexity of Bayesian network learning.