Probabilistic inference in high-dimensional Bayesian networks is difficult because exact manipulation of the joint distribution scales exponentially with network size. We propose a decomposition framework based on directed convex subgraphs and introduce a minimal d-decomposition tree.
arXiv:2509.13267v3 Announce Type: replace-cross
Abstract: A discrete Bayesian network is a directed acyclic graph (DAG) consisting of categorical variables. Two popular approaches for DBN modeling in...
By Alexander Dombowsky, David B. Dunson
The paper introduces JSP-GFN, a Generative Flow Network that jointly infers the structure and parameters of a Bayesian Network. It sequentially generates a directed acyclic graph edge by edge and then samples the corresponding conditional probability parameters once the full structure is known. Experiments on simulated and real data show that JSP‑GFN accurately approximates the joint posterior and outperforms existing methods.
By Tristan Deleu, Mizu Nishikawa-Toomey, Jithendaraa Subramanian, Esmeralda S. Whitammer, Laurent Charlin, Yoshua Bengio
arXiv:2609.40024v1 Announce Type: new
Abstract: We develop a general method for amortized Bayesian inference on multilevel models of arbitrary structure. Given a generative model specified as a direc...
By Daniel Habermann, Andreas Bulling, Stefan T. Radev, Paul-Christian B\"urkner
We develop a general method for amortized Bayesian inference on multilevel models of arbitrary structure. Given a generative model specified as a directed acyclic graph, our method automatically deriv...
The paper presents a consensus framework for Bayesian Network fusion that balances dependency preservation with computational tractability by enforcing treewidth constraints. It introduces genetic algorithms featuring advanced initialization, specialized operators, and a tailored fitness function to prioritize shared structures among input networks. Experiments on synthetic and real-world BNs demonstrate that these genetic algorithms outperform adapted methods and greedy baselines.
By Pablo Torrijos, Jos\'e A. G\'amez, Jos\'e M. Puerta, Juan A. Aledo