Results 11 to 20 of about 10,870,567 (191)
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 +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 +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 +2 more
doaj +3 more sources
Joint posterior inference for latent Gaussian models with R-INLA [PDF]
Journal of Statistical Computation and Simulation, 2022 33 pages, 11 ...
Cristian Chiuchiolo +2 more
openaire +4 more sources
Competing risks joint models using R-INLA [PDF]
Statistical Modelling, 2020 The methodological advancements made in the field of joint models are numerous. None the less, the case of competing risks joint models has largely been neglected, especially from a practitioner's point of view. In the relevant works on competing risks joint models, the assumptions of a Gaussian linear longitudinal series and proportional cause ...
Van Niekerk, Janet +2 more
openaire +5 more sources
Fast and accurate Bayesian model criticism and conflict diagnostics using R‐INLA [PDF]
Stat, 2017 Bayesian hierarchical models are increasingly popular for realistic modelling and analysis of complex data. This trend is accompanied by the need for flexible, general and computationally efficient methods for model criticism and conflict detection. Usually, a Bayesian hierarchical model incorporates a grouping of the individual data points, as, for ...
Ferkingstad, Egil +2 more
core +8 more sources
Spatial modelling with R-INLA: A review [PDF]
, 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 +7 more
openaire +3 more sources
Spatio‐temporal occupancy models with INLA [PDF]
Methods in Ecology and EvolutionModern 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 +3 more
doaj +7 more sources
On the choice of the mesh for the analysis of geostatistical data using R-INLA [PDF]
Communications in Statistics - Theory and Methods, 2018 Many methods used in spatial statistics are computationally demanding, and so, the development of more computationally efficient methods has received attention. A important development is the integrated nested Laplace approximation method which is carry out Bayesian analysis more efficiently This method, for geostatistical data, is done considering the
Ana Julia Righetto +3 more
openaire +3 more sources
Modeling multivariate positive‐valued time series using R‐INLA [PDF]
Applied Stochastic Models in Business and Industry, 2022 AbstractIn this article, we describe fast Bayesian statistical analysis of vector positive‐valued time series, with application to interesting financial data streams. We discuss a flexible level correlated model (LCM) framework for building hierarchical models for vector positive‐valued time series.
Dutta, Chiranjit +2 more
openaire +3 more sources