Cover Image

Graphical Modelling without Independence Assumptions for Uncentered Data

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Andrew, Bailey, Westhead, David R., Cutillo, Luisa
Format:	Preprint
Published:	2024
Subjects:	Methodology Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Making Multi-Axis Models Robust to Multiplicative Noise: How, and Why?
by: Andrew, Bailey, et al.
Published: (2026)

GmGM: a Fast Multi-Axis Gaussian Graphical Model
by: Andrew, Bailey, et al.
Published: (2022)

Making Multi-Axis Gaussian Graphical Models Scalable to Millions of Cells
by: Andrew, Bailey, et al.
Published: (2024)

On Learning Causal Structures from Non-Experimental Data without Any Faithfulness Assumption
by: Lin, Hanti, et al.
Published: (2018)

Exact and Approximate MCMC for Doubly-intractable Probabilistic Graphical Models Leveraging the Underlying Independence Model
by: Chen, Yujie, et al.
Published: (2025)

Assumption-Lean and Data-Adaptive Post-Prediction Inference
by: Miao, Jiacheng, et al.
Published: (2023)

Weighting-Based Identification and Estimation in Graphical Models of Missing Data
by: Guo, Anna, et al.
Published: (2026)

Graphical Transformation Models
by: Herp, Matthias, et al.
Published: (2025)

Profile Graphical Models
by: Avalos-Pacheco, Alejandra, et al.
Published: (2026)

Independence Testing for Temporal Data
by: Shen, Cencheng, et al.
Published: (2019)

Independent Component Discovery in Temporal Count Data
by: Chaussard, Alexandre, et al.
Published: (2026)

Assumption violations in causal discovery and the robustness of score matching
by: Montagna, Francesco, et al.
Published: (2023)

Centrum: Model-based Database Auto-tuning with Minimal Distributional Assumptions
by: Lai, Yuanhao, et al.
Published: (2025)

Choice of Scoring Rules for Indirect Elicitation of Properties with Parametric Assumptions
by: Hu, Lingfang, et al.
Published: (2025)

Sparse Gaussian Graphical Models with Discrete Optimization: Computational and Statistical Perspectives
by: Behdin, Kayhan, et al.
Published: (2023)

A General Approach to Visualizing Uncertainty in Statistical Graphics
by: Petek, Bernarda, et al.
Published: (2025)

$t$-Testing the Waters: Empirically Validating Assumptions for Reliable A/B-Testing
by: Jeunen, Olivier
Published: (2025)

High-Dimensional Undirected Graphical Models for Arbitrary Mixed Data
by: Göbler, Konstantin, et al.
Published: (2022)

Covariate-dependent Graphical Model Estimation via Neural Networks with Statistical Guarantees
by: Lin, Jiahe, et al.
Published: (2025)

High-dimensional Functional Graphical Model Structure Learning via Neighborhood Selection Approach
by: Zhao, Boxin, et al.
Published: (2021)

Probabilistic Multilabel Graphical Modelling of Motif Transformations in Symbolic Music
by: Taieb, Ron, et al.
Published: (2026)

Assumption-free stability for ranking problems
by: Liang, Ruiting, et al.
Published: (2025)

PAIR-CI: Calibrated Conditional Independence Testing for Causal Discovery with Incomplete Data
by: Robinson, Thomas S., et al.
Published: (2026)

A Copula Graphical Model for Multi-Attribute Data using Optimal Transport
by: Zhang, Qi, et al.
Published: (2024)

Probabilistic Graphical Model using Graph Neural Networks for Bayesian Inversion of Discrete Structural Component States
by: Li, Teng, et al.
Published: (2026)

LARGE: A Locally Adaptive Regularization Approach for Estimating Gaussian Graphical Models
by: Nguyen, Ha, et al.
Published: (2026)

Wrong Model, Right Uncertainty: Spatial Associations for Discrete Data with Misspecification
by: Burt, David R., et al.
Published: (2025)

A Graphical Approach to State Variable Selection in Off-policy Learning
by: Andersen, Joakim Blach, et al.
Published: (2025)

LOCO Feature Importance Inference without Data Splitting via Minipatch Ensembles
by: Gan, Luqin, et al.
Published: (2022)

Scalable Bayesian Structure Learning for Gaussian Graphical Models Using Marginal Pseudo-likelihood
by: Mohammadi, Reza, et al.
Published: (2023)

Empirically Calibrated Conditional Independence Tests
by: Pan, Milleno, et al.
Published: (2026)

On the Hardness of Conditional Independence Testing In Practice
by: He, Zheng, et al.
Published: (2025)

Assumption-Lean Post-Integrated Inference with Surrogate Control Outcomes
by: Du, Jin-Hong, et al.
Published: (2024)

Likelihood Based Inference in Fully and Partially Observed Exponential Family Graphical Models with Intractable Normalizing Constants
by: Chen, Yujie, et al.
Published: (2024)

rags2ridges: A One-Stop-Shop for Graphical Modeling of High-Dimensional Precision Matrices
by: Peeters, Carel F. W., et al.
Published: (2020)

Doubly Robust Conditional Independence Testing with Generative Neural Networks
by: Zhang, Yi, et al.
Published: (2024)

Causal Discovery via Conditional Independence Testing with Proxy Variables
by: Liu, Mingzhou, et al.
Published: (2023)

Data-Driven Approaches for Modelling Target Behaviour
by: Schlangen, Isabel, et al.
Published: (2024)

A Randomized Permutation Whole-Model Test Heuristic for Self-Validated Ensemble Models (SVEM)
by: Karl, Andrew T.
Published: (2024)

Incorporating Interventional Independence Improves Robustness against Interventional Distribution Shift
by: Sreekumar, Gautam, et al.
Published: (2025)