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); however they often incur a high computational cost.
Jamil, Haziq, Rue, Haavard
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Spatial seismic point pattern analysis with Integrated Nested Laplace Approximation
, 2020 This paper proposes the use of Integrated Nested Laplace Approximation (Rue et al., 2009) to describe the spatial displacement of earthquake data. Specifying a hiechical structure of the data and parameters, an inhomogeneuos Log-Gaussian Cox Processes ...
Giada Adelfio +2 more
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Bayesian models for missing and misclassified variables using integrated nested Laplace approximations [PDF]
Misclassified variables used in regression models, either as a covariate or as the response, may lead to biased estimators and incorrect inference. Even though Bayesian models to adjust for misclassification error exist, it has not been shown how these ...
Bastos, Leonardo Soares +3 more
core +4 more sources
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
Geographical Pattern of Testicular Cancer Points to Maternal Exposures Behind Increasing Incidence. [PDF]
Int J CancerABSTRACT Evidence suggests that testicular cancer (TC) originates from germ cell neoplasia in situ arising during fetal development, implying that prenatal environmental exposures may influence disease risk. We investigated whether maternal place of residence during pregnancy is associated with regional variation in TC incidence.
Kaipia A +8 more
europepmc +2 more sources
Fitting double hierarchical models with the integrated nested Laplace approximation
Statistics and Computing, 2022 AbstractDouble hierarchical generalized linear models (DHGLM) are a family of models that are flexible enough as to model hierarchically the mean and scale parameters. In a Bayesian framework, fitting highly parameterized hierarchical models is challenging when this problem is addressed using typical Markov chain Monte Carlo (MCMC) methods due to the ...
Mabel Morales-Otero +2 more
openaire +3 more sources
Parallelized integrated nested Laplace approximations for fast Bayesian inference
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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Markov chain Monte Carlo with the Integrated Nested Laplace Approximation [PDF]
Statistics and Computing, 2017 The Integrated Nested Laplace Approximation (INLA) has established itself as a widely used method for approximate inference on Bayesian hierarchical models which can be represented as a latent Gaussian model (LGM). INLA is based on producing an accurate approximation to the posterior marginal distributions of the parameters in the model and some other ...
Virgilio Gómez-Rubio, Håvard Rue
openaire +5 more sources
An Introduction to Predictive Processing Models of Perception and Decision‐Making
Topics in Cognitive Science, EarlyView., 2023 Abstract The predictive processing framework includes a broad set of ideas, which might be articulated and developed in a variety of ways, concerning how the brain may leverage predictive models when implementing perception, cognition, decision‐making, and motor control.
Mark Sprevak, Ryan Smith
wiley +1 more source
Using INLA to fit a complex point process model with temporally varying effects – a case study [PDF]
, 2012 Integrated nested Laplace approximation (INLA) provides a fast and yet quite exact approach to fitting complex latent Gaussian models which comprise many statistical models in a Bayesian context, including log Gaussian Cox processes. This paper discusses
Soerbye, S +3 more
core +2 more sources