arXiv:2607. 04650v1 Announce Type: cross Abstract: Probabilistic inference in high-dimensional Bayesian networks is difficult because exact manipulation of the joint distribution scales exponentially with network size.
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.
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:2608. 12640v1 Announce Type: cross Abstract: Causal discovery aims to uncover the underlying causal relationships given data generated from a system.
arXiv:2607. 22934v1 Announce Type: cross Abstract: Learning causal graphs from interventional data is a challenging problem with broad applications.
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...
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...
arXiv:2606. 11831v1 Announce Type: cross Abstract: Neural relational inference (NRI) methods discover interaction graphs from trajectories through variational reasoning on discrete potential edges.
The paper introduces a variational inference framework that jointly discovers latent clusters of variables and the causal relationships among those clusters. It models clusters with categorical distributions and graph structures with Bernoulli distributions, deriving variational lower bounds and estimation techniques for learning both cluster assignments and causal links. The method’s effectiveness is shown on synthetic and real datasets.
arXiv:2606. 06440v1 Announce Type: new Abstract: Data-driven causal relationship identification is pertinent to advancing understanding of complex systems both within and beyond science.
arXiv:2608.23802v1 Announce Type: cross Abstract: Many common data dependencies can be characterized by graphs: time series data are sequential (chain graph), images appear as pixels (lattice graph),...
The paper introduces Variational Bayesian Flow Network (VBFN), a graph generation model that lifts Bayesian updates to a joint Gaussian belief family with structured precisions, enabling coupled node and edge updates in a single fusion step. By constructing sample‑agnostic sparse precisions from a representation‑induced dependency graph, VBFN avoids label leakage while enforcing node‑edge consistency. Experiments on synthetic and molecular graph datasets show that VBFN improves fidelity and diversity over baseline methods.