4 citations · 4 across the 6 of their papers we have counts for
7 papers
PyINLA: Fast Bayesian Inference for Latent Gaussian Models in Python
Esmail Abdul Fattah, Elias Krainski, Havard Rue
Bayesian inference often relies on Markov chain Monte Carlo (MCMC) methods, particularly required for non-Gaussian data families. When dealing with complex hierarchical models, the…
GPU-Accelerated Parallel Selected Inversion for Structured Matrices Using sTiles
Esmail Abdul Fattah, Hatem Ltaief, Havard Rue +1
Selected inversion is essential for applications such as Bayesian inference, electronic structure calculations, and inverse covariance estimation, where computing only specific ele…
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…
INLA+ -- Approximate Bayesian inference for non-sparse models using HPC
Esmail Abdul-Fattah, Janet Van Niekerk, Haavard Rue
The integrated nested Laplace approximations (INLA) method has become a widely utilized tool for researchers and practitioners seeking to perform approximate Bayesian inference acr…
Non-stationary Bayesian Spatial Model for Disease Mapping based on Sub-regions
Esmail Abdul Fattah, Elias Krainski, Janet van Niekerk +1
This paper aims to extend the Besag model, a widely used Bayesian spatial model in disease mapping, to a non-stationary spatial model for irregular lattice-type data. The goal is t…
Approximate Bayesian Inference for the Interaction Types 1, 2, 3 and 4 with Application in Disease Mapping
Esmail Abdul Fattah, Haavard Rue
We address in this paper a new approach for fitting spatiotemporal models with application in disease mapping using the interaction types 1,2,3, and 4. When we account for the spat…