Bayesian Network Structure Learning

Latest papers

13 papers3 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Semi-Linear Gaussian Bayesian Networks: Enhancing Flexibility in Node Dependencies via penalized B-Splines
Oct 22, 2026Aitor Oviedo-Madrid, Mabel Morales-Otero, Jose Gonzalez-Gomariz +1Bayesian Network Structure Learning Bayesian Networks
Institute of Data Science and Artiﬁcial Intelligence (DATAI), University of Navarra, Pamplona, Spain · TECNUN School of Engineering, University of Navarra, San Sebastián, Spain
Generalizing Metropolis-Hastings for DAG Sampling
Oct 21, 2026Daniele Nikzad, Mikko KoivistoBayesian Network Structure Learning Markov Chain Monte Carlo
Department of Computer Science, University of Helsinki, Helsinki, FI-00014, Finland
Directional Evidence Guided Search-Space Reduction for Exact DAG Learning
Oct 6, 2026Upala Junaida Islam, Abdelmonem Elrefaey, Rong PanScore-Based Causal Discovery Bayesian Network Structure Learning
School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ 85281, USA.
ABSOL: Aggregated Bayesian Subsampling Orchestrated with LLMs
Sep 14, 2026Jackson Hassell, Chen Shen, Estevam HruschkaGraph Structure Learning Bayesian Network Structure Learning
Megagon Labs
LLM-Augmented Causal Discovery: Probabilistic Fusion of Edge Existence and Orientation
Aug 19, 2026Neville K. Kitson, Anthony ConstantinouBayesian Network Structure Learning Causal Discovery
Machine Intelligence and Decision Systems (MInDS) Group, School of Electronic Engineering and Computer Science, Queen Mary University of London, London, United Kingdom, E1 4NS.
LAB-Tab: LLM-Augmented Bayesian Network Adaptation for Few-Shot Tabular Generation
Aug 3, 2026Zijian Shen, Taijie Chen, Bin Zhou +2Bayesian Network Structure Learning Bayesian Networks
Department of Civil Engineering, The University of Hong Kong
Structured Inference with Large Language Gibbs
Jun 17, 2026Sanghyeok Choi, Henry Gouk, Esmeralda S. WhitammerMarkov Chain Monte Carlo Bayesian Network Structure Learning
University of Edinburgh, School of Informatics · 2CIFAR Fellow, Learning in Machines and Brains
KG-SoftMAP: Soft Knowledge-Graph Priors for Bayesian Network Structure Learning from Sparse Discrete Data
Jun 9, 2026Guoliang Xu, James E. CorterGraph Structure Learning Bayesian Network Structure Learning
Columbia University
Exact and Approximate Algorithms for Polytree Learning
May 5, 2026Juha Harviainen, Frank Sommer, Manuel SorgeGraph Structure Learning Bayesian Networks
Department of Computer Science, University of Helsinki, Helsinki, Finland · Institute of Computer Science, Friedrich-Schiller Universität Jena, Jena, Germany · Institute of Logic and Computation, TU Wien, Vienna, Austria
Time series causal discovery with variable lags
Apr 15, 2026Bruno Petrungaro, Anthony C. ConstantinouTime-Series Causal Discovery Causal Structure Learning
Bayesian Artificial Intelligence research lab, MInDS research group, Queen Mary University of London, Mile End Road, London, E1 4NS, UK.
Smoothing the Landscape: Causal Structure Learning via Diffusion Denoising Objectives
Apr 2, 2026Hao Zhu, Di Zhou, Donna SlonimCausal Discovery Causal Structure Learning
Beth Israel Deaconess Medical Center Harvard Medical School Boston, MA, USA · Department of Computer Science Tufts University Medford, MA, USA
Transfer learning for nonparametric Bayesian networks
Apr 1, 2026Rafael Sojo, Pedro Larrañaga, Concha BielzaBayesian Network Structure Learning Transfer Learning
Aingura IIoT, Paseo Mikeletegui 43, 20009 Donostia-San Sebastián, Gipuzkoa, Spain · Universidad Politécnica de Madrid, Departamento de Inteligencia Artificial, 28660 Boadilla del Monte, Madrid, Spain
Learning Bayesian and Markov Networks with an Unreliable Oracle
Mar 10, 2026Juha Harviainen, Pekka Parviainen, Vidya Sagar SharmaGraph Structure Learning Bayesian Network Structure Learning
University of Helsinki, Finland · University of Bergen, Norway · Indian Institute of Technology Madras, India

Oct 22, 2026J/K move · Enter open · S save

Semi-Linear Gaussian Bayesian Networks: Enhancing Flexibility in Node Dependencies via penalized B-Splines

Aitor Oviedo-Madrid, Mabel Morales-Otero, Jose Gonzalez-Gomariz, Ruben Armañanzas

Institute of Data Science and Artiﬁcial Intelligence (DATAI), University of Navarra, Pamplona, Spain · TECNUN School of Engineering, University of Navarra, San Sebastián, Spain

Bayesian Network Structure Learning Bayesian Networks Semiparametric Inference

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Bayesian networks for continuous variables often rely on restrictive assumptions: linear Gaussian models lack flexibility to capture nonlinear dependencies, while non-parametric approaches provide greater flexibility at a high computational cost. This creates a gap for modeling scenarios where variables are approximately Gaussian but exhibit nonlinear relationships. We introduce Semi-Linear Gaussian Bayesian Networks (SLGBN), a novel class of continuous Bayesian networks designed to bridge this gap, preserving Gaussian conditional distributions while introducing nonlinear conditional mean functions. The proposed framework provides a unified probabilistic framework combining linear Gaussian and nonlinear Gaussian conditional models within the same Bayesian network Nonlinear conditional mean functions are estimated using generalized additive models (GAMs) with penalized B-splines. We present parameter estimation procedures for both types of nodes, as well as a greedy hill-climbing algorithm for learning the network structure under the SLGBN framework. To assess its performance, we compare SLGBN with other continuous Bayesian network models, including semiparametric Bayesian networks (SPBN) and Linear Gaussian Bayesian networks (LGBN). Experimental results evaluate both goodness of fit and the ability to recover the underlying network structure. The results show that, in the presence of nonlinear Gaussian relationships, SLGBN achieves better fit and improved structure recovery compared to alternative models, particularly when combined with the Extended Bayesian Information Criterion (EBIC*) score. Furthermore, by retaining Gaussian conditional distributions, SLGBN offers computational advantages over fully semiparametric approaches.