50 citations · 90 across the 22 of their papers we have counts for
24 papers
Dense Matrices Are Alike; Sparse Matrices Are Sparse in Their Own Way: A Structure-Adaptive Tile Cholesky Factorization
Esmail Abdul Fattah, Hatem Ltaief, Håvard Rue +1
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 with…
Cross Validation for the log Gaussian Cox Process
Hans Montcho, Håvard Rue, Finn Lindgren +4
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 n…
Deterministic Leave-One-Cluster-Out Cross-Validation for Multilevel Bayesian Structural Equation Models
Mohammad Alhyari, Haziq Jamil, Hans Montcho +1
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…
Distributed and recursive Bayesian inference for Big Data and complex spatio-temporal models
Mario Figueira, David Conesa, Antonio López-Quílez +1
The rapid growth of massive and complex datasets in fields such as econometrics, environmental sciences, risk management, and public policy has reshaped statistical modeling while…
Informative Distance-Based Priors for Correlation Matrices Centred on a Target Reference
Anna Freni-Sterrantino, Janet van Niekerk, Elias Teixeira Krainski +3
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 th…
ADELIA: Automatic Differentiation for Efficient Laplace Inference Approximations
Afif Boudaoud, Lisa Gaedke-Merzhäuser, Alexandros Nikolaos Ziogas +6
Spatio-temporal Bayesian inference drives environmental and health sciences using latent Gaussian models. Integrated Nested Laplace Approximations (INLA) enable inference for these…
Accelerated Spatio-Temporal Bayesian Modeling for Multivariate Gaussian Processes
Lisa Gaedke-Merzhäuser, Vincent Maillou, Fernando Rodriguez Avellaneda +5
Multivariate Gaussian processes (GPs) offer a powerful probabilistic framework to represent complex interdependent phenomena. They pose, however, significant computational challeng…
Scalable skewed Bayesian inference for latent Gaussian models
Shourya Dutta, Janet van Niekerk, Haavard Rue
Approximate Bayesian inference for the class of latent Gaussian models can be achieved efficiently with integrated nested Laplace approximations (INLA). Based on recent reformulati…
sTiles: An Accelerated Computational Framework for Sparse Factorizations of Structured Matrices
Esmail Abdul Fattah, Hatem Ltaief, Havard Rue +1
This paper introduces sTiles, a GPU-accelerated framework for factorizing sparse structured symmetric matrices. By leveraging tile algorithms for fine-grained computations, sTiles…
Spatio-temporal Occupancy Models with INLA
Jafet Belmont, Sara Martino, Janine Illian +1
Modern methods for quantifying and predicting species distribution play a crucial part in biodiversity conservation. Occupancy models are a popular choice for analyzing species occ…
Enhanced spatial modeling on linear networks using Gaussian Whittle-Matérn fields
Somnath Chaudhuri, Maria A. Barceló, Pablo Juan +4
Spatial statistics is traditionally based on stationary models on like Matérn fields. The adaptation of traditional spatial statistical methods, originally designed…
A graphical framework for interpretable correlation matrix models
Anna Freni Sterrantino, Denis Rustand, Janet van Niekerk +2
In this work, we present a new approach for constructing models for correlation matrices with a user-defined graphical structure. The graphical structure makes correlation matrices…
Parallel Selected Inversion for Space-Time Gaussian Markov Random Fields
Abylay Zhumekenov, Elias T. Krainski, Håvard Rue
Performing Bayesian inference on large spatio-temporal models requires extracting inverse elements of large sparse precision matrices for marginal variances, as well as estimating…
Bayesian Inference for Multivariate Spatial Models with R-INLA
Francisco Palmí-Perales, Virgilio Gómez-Rubio, Roger S Bivand +2
Bayesian methods and software for spatial data analysis are generally now well established in the scientific community. Despite the wide application of spatial models, the analysis…
Bayesian survival analysis with INLA
Danilo Alvares, Janet van Niekerk, Elias Teixeira Krainski +2
This tutorial shows how various Bayesian survival models can be fitted using the integrated nested Laplace approximation in a clear, legible, and comprehensible manner using the IN…
Fitting latent non-Gaussian models using variational Bayes and Laplace approximations
Rafael Cabral, David Bolin, Håvard Rue
Latent Gaussian models (LGMs) are perhaps the most commonly used class of models in statistical applications. Nevertheless, in areas ranging from longitudinal studies in biostatist…
Controlling the flexibility of non-Gaussian processes through shrinkage priors
Rafael Cabral, David Bolin, Håvard Rue
The normal inverse Gaussian (NIG) and generalized asymmetric Laplace (GAL) distributions can be seen as skewed and semi-heavy-tailed extensions of the Gaussian distribution. Models…
Joint Quantile Disease Mapping with Application to Malaria and G6PD Deficiency
Hanan Alahmadi, Håvard Rue, Janet van Niekerk
Statistical analysis based on quantile regression methods is more comprehensive, flexible, and less sensitive to outliers when compared to mean regression methods. When the link be…
The Bayesian Learning Rule
Mohammad Emtiyaz Khan, Håvard Rue
We show that many machine-learning algorithms are specific instances of a single algorithm called the \emph{Bayesian learning rule}. The rule, derived from Bayesian principles, yie…
Smart Gradient -- An Adaptive Technique for Improving Gradient Estimation
Esmail Abdul Fattah, Janet Van Niekerk, Haavard Rue
Computing the gradient of a function provides fundamental information about its behavior. This information is essential for several applications and algorithms across various field…
Discrete versus continuous domain models for disease mapping
Garyfallos Konstantinoudis, Dominic Schuhmacher, Håvard Rue +1
The main goal of disease mapping is to estimate disease risk and identify high-risk areas. Such analyses are hampered by the limited geographical resolution of the available data.…
Spatial modelling with R-INLA: A review
Haakon Bakka, Håvard Rue, Geir-Arne Fuglstad +5
Coming up with Bayesian models for spatial data is easy, but performing inference with them can be challenging. Writing fast inference code for a complex spatial model with realist…
Estimating animal abundance with N-mixture models using the R-INLA package for R
Timothy D. Meehan, Nicole L. Michel, Håvard Rue
Successful management of wildlife populations requires accurate estimates of abundance. Abundance estimates can be confounded by imperfect detection during wildlife surveys. N-mixt…
Improving the INLA approach for approximate Bayesian inference for latent Gaussian models
Egil Ferkingstad, Håvard Rue
We introduce a new copula-based correction for generalized linear mixed models (GLMMs) within the integrated nested Laplace approximation (INLA) approach for approximate Bayesian i…