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H. Rue

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Preprint Sep 2026

Deterministic Leave-One-Cluster-Out Cross-Validation for Multilevel Bayesian Structural Equation Models

We introduce a closed-form, refit-free procedure for leave-one-cluster-out (LOCO) cross-validation in multilevel Gaussian Bayesian structural equation models (SEMs), together with predictive scoring of every nested submodel. Conditional independence of clusters given the parameters expresses the cluster-deleted posteri...

Mohammad Alhyari, Haziq Jamil, Hans Montcho et al. · 0 citations
Preprint Sep 2026

Cross Validation for the log Gaussian Cox Process

The log Gaussian Cox Process (LGCP) is one of the most widely used models for the analysis of spatial point patterns. Although Bayesian methods and software for fitting LGCPs are now well established, practical tools for model criticism, predictive assessment, and model comparison remain comparatively underdeveloped. T...

Hans Montcho, Håvard Rue, F. Lindgren et al. · 0 citations
Preprint Sep 2026

Dense Matrices Are Alike; Sparse Matrices Are Sparse in Their Own Way: A Structure-Adaptive Tile Cholesky Factorization

Sparse direct Cholesky solvers fix one data structure for an entire matrix, but symmetric positive definite systems range from nearly dense to irregular, sometimes mixing both within one matrix. We let the data structure follow the sparsity structure, across matrices and across tiles within a matrix. Before numerical w...

Esmail Abdul Fattah, H. Ltaief, Håvard Rue et al. · 0 citations
Open access Sep 2026

Advancing multilevel Bayesian networks with efficient Bayesian inference

This article proposes an innovative framework, INLA-MBN, that integrates multilevel BNs (MBNs) with the integrated nested Laplace approximation (INLA), thereby facilitating efficient structure and parameter learning in both longitudinal and cross-sectional multilevel data contexts.

B. E. Yirdaw, L. K. Debusho, J. van Niekerk et al. · 0 citations
Preprint Jul 2026

Informative Distance-Based Priors for Correlation Matrices Centred on a Target Reference

Specifying a prior over the space of correlation matrices is a persistent challenge in Bayesian analysis. The space is a curved manifold whose dimension grows quadratically with the number of variables, making substantive prior beliefs difficult to encode.\\ We propose a distance-based prior that assigns mass decaying...

A. Freni-Sterrantino, J. V. Niekerk, E. Krainski et al. · 1 citation

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