Bayesian spatial modelling of geostatistical data using INLA and SPDE methods: A case study predicting malaria risk in Mozambique
P Moraga, C Dean, J Inoue, P Morawiecki… - Spatial and Spatio …, 2021 - Elsevier
Bayesian spatial models are widely used to analyse data that arise in scientific disciplines
such as health, ecology, and the environment. Traditionally, Markov chain Monte Carlo
(MCMC) methods have been used to fit these type of models. However, these are highly
computationally intensive methods that present a wide range of issues in terms of
convergence and can become infeasible in big data problems. The integrated nested
Laplace approximation (INLA) method is a computational less-intensive alternative to …
such as health, ecology, and the environment. Traditionally, Markov chain Monte Carlo
(MCMC) methods have been used to fit these type of models. However, these are highly
computationally intensive methods that present a wide range of issues in terms of
convergence and can become infeasible in big data problems. The integrated nested
Laplace approximation (INLA) method is a computational less-intensive alternative to …
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