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
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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
doaj +5 more sources
Bayesian Integrated Nested Laplace Approximation (INLA) Longevity Bonds Market Model
RisksPricing coupon longevity bonds (CLBs) is challenging in illiquid markets due to the incompleteness of insurance markets and the unavailability of longevity payout data.
Yethu Sithole, Samuel Asante Gyamerah
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Importance Sampling with the Integrated Nested Laplace Approximation [PDF]
Journal of Computational and Graphical Statistics, 2022 The Integrated Nested Laplace Approximation (INLA) is a deterministic approach to Bayesian inference on latent Gaussian models (LGMs) and focuses on fast and accurate approximation of posterior marginals for the parameters in the models. Recently, methods have been developed to extend this class of models to those that can be expressed as conditional ...
Martin Outzen Berild +3 more
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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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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 +3 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
Application of integrated nested Laplace approximation to identify hot spots of methylation heterogeneity in healthy individuals from the MAMELI cohort [PDF]
Frontiers in GeneticsDNA methylation is an epigenetic regulator of gene expression and cell identity, which can be shaped by both physiological and pathological factors, including environmental exposure.
Tiago Nardi +7 more
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An Approximate Bayesian Inference for Beta Regression Models [PDF]
Journal of Sciences, Islamic Republic of Iran, 2022 In modeling the variables related to each other, regression models are usually used assuming that the response variable is Normal. But in problems dealing with data such as the rate or ratio of an event distributed in the (0,1) interval, these models may
kobra Gholizadeh Gazvar +1 more
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