Estimating Spatial Econometrics Models with Integrated Nested Laplace Approximation [PDF]
Mathematics, 2021 The integrated nested Laplace approximation (INLA) provides a fast and effective method for marginal inference in Bayesian hierarchical models. This methodology has been implemented in the R-INLA package which permits INLA to be used from within R ...
Virgilio Gómez-Rubio +2 more
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Bayesian Model Averaging with the Integrated Nested Laplace Approximation [PDF]
Econometrics, 2020 The integrated nested Laplace approximation (INLA) for Bayesian inference is an efficient approach to estimate the posterior marginal distributions of the parameters and latent effects of Bayesian hierarchical models that can be expressed as latent ...
Virgilio Gómez-Rubio +2 more
doaj +7 more sources
Efficient and flexible Integration of variant characteristics in rare variant association studies using integrated nested Laplace approximation. [PDF]
PLoS Computational Biology, 2021 Rare variants are thought to play an important role in the etiology of complex diseases and may explain a significant fraction of the missing heritability in genetic disease studies. Next-generation sequencing facilitates the association of rare variants
Hana Susak +9 more
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Simplified Integrated Nested Laplace Approximation [PDF]
Biometrika, 2019 Integrated nested Laplace approximation provides accurate and efficient approximations for marginal distributions in latent Gaussian random field models. Computational feasibility of the original Rue et al.
Wood, Simon N
core +8 more sources
Predicting spatio-temporal dynamics of dengue using INLA (integrated nested laplace approximation) in Yogyakarta, Indonesia [PDF]
BMC Public HealthIntroduction Dengue is a mosquito-borne disease caused by the dengue virus, primarily transmitted by Aedes aegypti and Aedes albopictus. Its incidence fluctuates due to spatial and temporal factors, necessitating robust modeling approaches for prediction
Marko Ferdian Salim +2 more
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The integrated nested Laplace approximation applied to spatial log-Gaussian Cox process models. [PDF]
J Appl Stat, 2023 Spatial point process models are theoretically useful for mapping discrete events, such as plant or animal presence, across space; however, the computational complexity of fitting these models is often a barrier to their practical use. The log-Gaussian Cox process (LGCP) is a point process driven by a latent Gaussian field, and recent advances have ...
Flagg K, Hoegh A.
europepmc +4 more sources
Estimating the expected value of partial perfect information in health economic evaluations using integrated nested Laplace approximation. [PDF]
Stat Med, 2016 The Expected Value of Perfect Partial Information (EVPPI) is a decision-theoretic measure of the "cost" of parametric uncertainty in decision making used principally in health economic decision making.
Heath A, Manolopoulou I, Baio G.
europepmc +6 more sources
A review of R-packages for random-intercept probit regression in small clusters [PDF]
Frontiers in Applied Mathematics and Statistics, 2016 Generalized Linear Mixed Models (GLMMs) are widely used to model clustered categorical outcomes. To tackle the intractable integration over the random effects distributions, several approximation approaches have been developed for likelihood-based ...
Haeike Josephy, Tom Loeys, Yves Rosseel
doaj +5 more sources
Spatial Bayesian Hierarchical Modelling with Integrated Nested Laplace Approximation
, 2020 We consider latent Gaussian fields for modelling spatial dependence in the context of both spatial point patterns and areal data, providing two different applications. The inhomogeneous Log-Gaussian Cox Process model is specified to describe a seismic sequence occurred in Greece, resorting to the Stochastic Partial Differential Equations.
D'Angelo N, Abbruzzo A, Adelfio G.
europepmc +3 more sources
Bayesian predictive modelling to ascertain factors affecting cattle milk production in Tanzania: Evidence from the national panel surveys 2012 – 2021 [PDF]
Veterinary and Animal ScienceThis study aimed to evaluate and compare Bayesian predictive models to identify and quantify the key household inputs affecting cattle milk production in Tanzania.
Zainabu Bonza +2 more
doaj +2 more sources