The paper introduces Semi-Linear Gaussian Bayesian Networks (SLGBN), a new class of continuous Bayesian networks that combine linear Gaussian models with nonlinear conditional mean functions estimated via penalized B-splines within generalized additive models. SLGBN preserves Gaussian conditional distributions while allowing nonlinear relationships, and the authors provide parameter estimation procedures and a greedy hill‑climbing algorithm for structure learning. Experiments comparing SLGBN to semiparametric and linear Gaussian models show that SLGBN achieves better goodness‑of‑fit and structure recovery, especially when using the EBIC* score, while maintaining computational efficiency.
arXiv:2503. 10496v2 Announce Type: replace-cross Abstract: Modeling natural phenomena with artificial neural networks (ANNs) often provides highly accurate predictions.
The paper introduces Dirichlet Process Mixtures (DPMs) as a new non‑parametric conditional probability distribution (CPD) model for Semiparametric Bayesian Networks (SPBNs). By replacing kernel density estimators with DPMs, the authors achieve a more compact representation that reduces inference costs and improves density estimation accuracy. Experiments on synthetic and real‑world data show that DPM‑based SPBNs outperform existing SPBNs and other continuous‑variable Bayesian network models in both density estimation and structure learning tasks.
arXiv:2610.16902v1 Announce Type: new Abstract: Bayesian Networks (BNs) are established tools for letting agents reason under uncertainty in sequential decision-making. They split a joint distributio...
arXiv:2609.13655v1 Announce Type: new Abstract: On evolving graphs, node classifiers must satisfy two key requirements: inductive generalization to newly arriving nodes under distribution shift and c...
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.
arXiv:2607. 25376v1 Announce Type: cross Abstract: In Bayesian neural networks (BNNs), variational inference is a widely adopted framework for modeling uncertainty in a distributional way, with the evidence lower bound (ELBO) serving as the standard objective function.
arXiv:2404.17763v3 Announce Type: replace-cross Abstract: Probabilistic graphical models that encode an underlying Markov random field are fundamental building blocks of generative modeling to learn...
arXiv:2609.21673v1 Announce Type: new Abstract: Probabilistic Graphical Models (PGMs), especially Bayesian Networks (BNs), expose directed structure and probabilistic parameters, making them natural...
arXiv:2601. 07944v2 Announce Type: replace-cross Abstract: Since the turn of the century, approximate Bayesian inference has steadily evolved as new computational techniques have been incorporated to handle increasingly complex, large-scale predictive problems.
arXiv:2610.19074v1 Announce Type: new Abstract: Reliable uncertainty quantification remains an important challenge in AI. Hierarchical Bayesian methods provide a principled framework for this by repr...
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...
arXiv:2606. 19366v1 Announce Type: cross Abstract: Information lattice learning (ILL) learns interpretable rules of a signal by alternately projecting the signal onto a partition lattice that encodes a hierarchy of abstractions and lifting selected rules back to the signal domain.