Results 11 to 20 of about 6,671 (202)

Spatio‐temporal occupancy models with INLA [PDF]

Methods in Ecology and Evolution
Modern methods for quantifying, predicting and mapping species distributions have played a crucial part in biodiversity conservation. Occupancy models have become a popular choice for analysing species occurrence data due to their ability to separate out
Jafet Belmont, Sara Martino, Janine Illian, Håvard Rue   +3 more
doaj   +10 more sources

Unveiling the Mutations and Conservation of InlA in Listeria monocytogenes [PDF]

Microorganisms
Listeria monocytogenes (L. monocytogenes) is a pathogen that is transmitted through contaminated food and causes the illness known as listeriosis. The virulence factor InlA plays a crucial role in the invasion of L.
Lingling Li, Yan Wang, Ji Pu, Jinni Chen, Lingyun Liu, Pan Mao, Hui Sun, Xia Luo, Changyun Ye   +8 more
doaj   +4 more sources

Bayesian Spatial Modelling with R-INLA [PDF]

Journal of Statistical Software, 2015
The principles behind the interface to continuous domain spatial models in the R- INLA software package for R are described. The integrated nested Laplace approximation (INLA) approach proposed by Rue, Martino, and Chopin (2009) is a computationally ...
Finn Lindgren, Håvard Rue
doaj   +9 more sources

Bayesian Inference for Multivariate Spatial Models with INLA [PDF]

The R Journal, 2023
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 of multivariate spatial data using R-INLA has not been widely described in the existing literature.
Francisco Palmí-Perales, Virgilio Gómez-Rubio, Roger S. Bivand, Michela Cameletti, Håvard Rue   +4 more
openaire   +5 more sources

Spatial modeling with R‐INLA: A review [PDF]

WIREs Computational Statistics, 2018
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 realistically‐sized datasets from scratch is time‐consuming, and if changes are made to the model, there is little guarantee that the code performs well.
Bakka, Haakon, Rue, Håvard, Fuglstad, Geir-Arne, Riebler, Andrea, Bolin, David, Illian, Janine, Krainski, Elias, Simpson, Daniel, Lindgren, Finn   +8 more
core   +9 more sources

Spatial and spatio-temporal models with R-INLA [PDF]

Spatial and Spatio-temporal Epidemiology, 2013
During the last three decades, Bayesian methods have developed greatly in the field of epidemiology. Their main challenge focusses around computation, but the advent of Markov Chain Monte Carlo methods (MCMC) and in particular of the WinBUGS software has opened the doors of Bayesian modelling to the wide research community. However model complexity and
Blangiardo, Marta, CAMELETTI, Michela, Baio, Gianluca, Rue, Håvard   +3 more
core   +10 more sources

Spatial Data Analysis with R-INLA with Some Extensions [PDF]

Journal of Statistical Software, 2015
The integrated nested Laplace approximation (INLA) provides an interesting way of approximating the posterior marginals of a wide range of Bayesian hierarchical models.
Roger Bivand, Virgilio Gómez-Rubio, Håvard Rue   +2 more
doaj   +3 more sources

Bayesian Computing with INLA: A Review [PDF]

Annual Review of Statistics and Its Application, 2017
The key operation in Bayesian inference is to compute high-dimensional integrals. An old approximate technique is the Laplace method or approximation, which dates back to Pierre-Simon Laplace (1774). This simple idea approximates the integrand with a second-order Taylor expansion around the mode and computes the integral analytically.
Rue, Håvard, Riebler, Andrea Ingeborg, Sørbye, Sigrunn Holbek, Illian, Janine B., Simpson, Daniel Peter, Lindgren, Finn Kristian   +5 more
openaire   +7 more sources

Spatio-temporal disease mapping using INLA [PDF]

Environmetrics, 2010
Spatio-temporal disease mapping models are a popular tool to describe the pattern of disease counts. They are usually formulated in a hierarchical Bayesian framework with latent Gaussian model. So far, computationally expensive Markov chain Monte Carlo algorithms have been used for parameter estimation which might induce a large Monte Carlo error.
Schrödle, B, Held, L
core   +3 more sources

An efficient PG-INLA algorithm for the Bayesian inference of logistic item response models [PDF]

Statistical Theory and Related Fields
In this paper, we propose a Bayesian PG-INLA algorithm which is tailored to both one-dimensional and multidimensional 2-PL IRT models. The proposed PG-INLA algorithm utilizes a computationally efficient data augmentation strategy via the Pólya-Gamma ...
Xiaofan Lin, Yincai Tang
doaj   +2 more sources