Image de couverture de livre

Probabilistic Graphical Model using Graph Neural Networks for Bayesian Inversion of Discrete Structural Component States

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Li, Teng, Wu, Stephen, Huang, Yong, Beck, James L., Li, Hui
Format:	Preprint
Publié:	2026
Sujets:	Machine Learning Methodology
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

MCMC with Adaptive Principal-Component Transformation: Rotation-Invariant Universal Samplers for Bayesian Structural System Identification
par: Meng, Xianghao, et autres
Publié: (2026)

Adaptive Meta-Learning Stochastic Gradient Hamiltonian Monte Carlo Simulation for Bayesian Updating of Structural Dynamic Models
par: Meng, Xianghao, et autres
Publié: (2026)

Probabilistic Multilabel Graphical Modelling of Motif Transformations in Symbolic Music
par: Taieb, Ron, et autres
Publié: (2026)

Sparse Gaussian Graphical Models with Discrete Optimization: Computational and Statistical Perspectives
par: Behdin, Kayhan, et autres
Publié: (2023)

Scalable Bayesian Structure Learning for Gaussian Graphical Models Using Marginal Pseudo-likelihood
par: Mohammadi, Reza, et autres
Publié: (2023)

Covariate-dependent Graphical Model Estimation via Neural Networks with Statistical Guarantees
par: Lin, Jiahe, et autres
Publié: (2025)

Hierarchical Probabilistic Principal Component Analysis of Longitudinal Data
par: Zhang, Xinyu, et autres
Publié: (2026)

Exact and Approximate MCMC for Doubly-intractable Probabilistic Graphical Models Leveraging the Underlying Independence Model
par: Chen, Yujie, et autres
Publié: (2025)

Bayesian Empirical Bayes: Simultaneous Inference from Probabilistic Symmetries
par: Wu, Bohan, et autres
Publié: (2025)

Graphical Principal Component Analysis of Multivariate Functional Time Series
par: Tan, Jianbin, et autres
Publié: (2024)

Structure Maintained Representation Learning Neural Network for Causal Inference
par: Sun, Yang, et autres
Publié: (2025)

Neural Bayes Estimators for Irregular Spatial Data using Graph Neural Networks
par: Sainsbury-Dale, Matthew, et autres
Publié: (2023)

Graphical Transformation Models
par: Herp, Matthias, et autres
Publié: (2025)

Amortized Inference for Correlated Discrete Choice Models via Equivariant Neural Networks
par: Huch, Easton, et autres
Publié: (2026)

High-dimensional Functional Graphical Model Structure Learning via Neighborhood Selection Approach
par: Zhao, Boxin, et autres
Publié: (2021)

Profile Graphical Models
par: Avalos-Pacheco, Alejandra, et autres
Publié: (2026)

A Copula Graphical Model for Multi-Attribute Data using Optimal Transport
par: Zhang, Qi, et autres
Publié: (2024)

Bayesian Functional Graphical Models with Change-Point Detection
par: Liu, Chunshan, et autres
Publié: (2024)

Change-point Detection and Segmentation of Discrete Data using Bayesian Context Trees
par: Lungu, Valentinian, et autres
Publié: (2022)

When Graph Neural Network Meets Causality: Opportunities, Methodologies and An Outlook
par: Jiang, Wenzhao, et autres
Publié: (2023)

A Graphical Approach to State Variable Selection in Off-policy Learning
par: Andersen, Joakim Blach, et autres
Publié: (2025)

Introducing Diminutive Causal Structure into Graph Representation Learning
par: Gao, Hang, et autres
Publié: (2024)

Graphical Modelling without Independence Assumptions for Uncentered Data
par: Andrew, Bailey, et autres
Publié: (2024)

Enhancing the Power of Gaussian Graphical Model Inference by Modeling the Graph Structure
par: Kilian, Valentin, et autres
Publié: (2024)

Weighting-Based Identification and Estimation in Graphical Models of Missing Data
par: Guo, Anna, et autres
Publié: (2026)

Sequential Conditional Transport on Probabilistic Graphs for Interpretable Counterfactual Fairness
par: Machado, Agathe Fernandes, et autres
Publié: (2024)

Bayesian Inference for PDE-based Inverse Problems using the Optimization of a Discrete Loss
par: Amoudruz, Lucas, et autres
Publié: (2025)

Nonlinear Bayesian optimal experimental design using logarithmic Sobolev inequalities
par: Li, Fengyi, et autres
Publié: (2024)

Bayesian Inference for Single-factor Graphical Models
par: Marcano, David, et autres
Publié: (2025)

Symbolic Density Estimation for Discrete Distributions
par: Liu, Ziwen, et autres
Publié: (2026)

Bayesian Multiplicity Correction in the Probabilistic Forward Stepwise Framework
par: Womack, Andrew, et autres
Publié: (2026)

Variational Bayesian Bow tie Neural Networks with Shrinkage
par: Sheinkman, Alisa, et autres
Publié: (2024)

Bayesian Structure Learning in Undirected Gaussian Graphical Models: Literature Review with Empirical Comparison
par: Vogels, Lucas, et autres
Publié: (2023)

High-Dimensional Covariate-Dependent Discrete Graphical Models and Dynamic Ising Models
par: Roach, Lyndsay, et autres
Publié: (2025)

Understanding the Trade-offs in Accuracy and Uncertainty Quantification: Architecture and Inference Choices in Bayesian Neural Networks
par: Sheinkman, Alisa, et autres
Publié: (2025)

Integrating Causal Inference with Graph Neural Networks for Alzheimer's Disease Analysis
par: Peddi, Pranay Kumar, et autres
Publié: (2025)

Bayesian Graphical High-Dimensional Time Series Models for Detecting Structural Changes
par: Ghosh, Shuvrarghya, et autres
Publié: (2025)

Mixture of Directed Graphical Models for Discrete Spatial Random Fields
par: Carter, J. Brandon, et autres
Publié: (2024)

Learning Gaussian Graphical Models under Total Positivity via Spectral Graph Sparsification
par: Rodríguez, Ignacio Echave-Sustaeta, et autres
Publié: (2026)

Near-Optimal Approximations for Bayesian Inference in Function Space
par: Wild, Veit, et autres
Publié: (2025)