Results 11 to 20 of about 3,307,614 (190)
Animal Models and Integrated Nested Laplace Approximations [PDF]
G3 Genes|Genomes|Genetics, 2013 AbstractAnimal models are generalized linear mixed models used in evolutionary biology and animal breeding to identify the genetic part of traits. Integrated Nested Laplace Approximation (INLA) is a methodology for making fast, nonsampling-based Bayesian inference for hierarchical Gaussian Markov models.
Holand, Anna Marie +3 more
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Simplified integrated nested Laplace approximation [PDF]
Biometrika, 2019 SummaryIntegrated 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. (2009) methods relies on efficient approximation of Laplace approximations for the marginal distributions of the coefficients of the latent ...
Wood, Simon N, Simon N Wood
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The Integrated Nested Laplace Approximation for Fitting Dirichlet Regression Models [PDF]
Journal of Computational and Graphical Statistics, 2023 This paper introduces a Laplace approximation to Bayesian inference in Dirichlet regression models, which can be used to analyze a set of variables on a simplex exhibiting skewness and heteroscedasticity, without having to transform the data. These data, which mainly consist of proportions or percentages of disjoint categories, are widely known as ...
Joaquín Martínez-Minaya +4 more
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Integrated Nested Laplace Approximation for Bayesian Nonparametric Phylodynamics [PDF]
, 2012 Appears in Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence (UAI2012)
Palacios, JA, Minin, VN
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Fitting complex ecological point process models with integrated nested Laplace approximation [PDF]
Methods in Ecology and Evolution, 2013 Summary We highlight an emerging statistical method, integrated nested Laplace approximation ( INLA ), which is ideally suited for fitting complex models to many of the rich spatial data ...
Illian, Janine Baerbel +6 more
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A novel method of marginalisation using low discrepancy sequences for integrated nested Laplace approximations [PDF]
Computational Statistics & Data Analysis, 2021 Recently, it has been shown that approximations to marginal posterior distributions obtained using a low discrepancy sequence (LDS) can outperform standard grid-based methods with respect to both accuracy and computational efficiency. This recent method, which we will refer to as LDS-StM, can also produce good approximations to multimodal posteriors ...
Paul T. Brown +3 more
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ISPRS International Journal of Geo-Information, 2016 This research investigates spatio-temporal patterns of police calls-for-service in the Region of Waterloo, Canada, at a fine spatial and temporal resolution. Modeling was implemented via Bayesian Integrated Nested Laplace Approximation (INLA).
Hui Luan, Matthew Quick, Jane Law
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Parallelized integrated nested Laplace approximations for fast Bayesian inference [PDF]
Statistics and Computing, 2022 There is a growing demand for performing larger-scale Bayesian inference tasks, arising from greater data availability and higher-dimensional model parameter spaces. In this work we present parallelization strategies for the methodology of integrated nested Laplace approximations (INLA), a popular framework for performing approximate Bayesian inference
Lisa Gaedke-Merzhäuser +3 more
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Multivariate Posterior Inference for Spatial Models with the Integrated Nested Laplace Approximation
Journal of the Royal Statistical Society Series C: Applied Statistics, 2018 SummaryThe integrated nested Laplace approximation (INLA) is a convenient way to obtain approximations to the posterior marginals for parameters in Bayesian hierarchical models when the latent effects can be expressed as a Gaussian Markov random field. In addition, its implementation in the R-INLA package for the R statistical software provides an easy
Gómez-Rubio, Virgilio +1 more
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Approximate Bayesian Inference for Structural Equation Models using Integrated Nested Laplace Approximations [PDF]
Markov chain Monte Carlo (MCMC) methods remain the mainstay of Bayesian estimation of structural equation models (SEM), though they often incur a high computational cost. We present a bespoke approximate Bayesian approach to SEM, drawing on ideas from the integrated nested Laplace approximation (INLA, Rue et al., 2009, J. R. Stat. Soc.
Jamil, Haziq, Rue, Håvard
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