Results 21 to 30 of about 3,307,614 (190)
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
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Determining factors associated with cholera disease in Ethiopia using Bayesian hierarchical modeling
BMC Public Health, 2022 Background Cholera is a diarrheal disease caused by infection of the intestine with the gram-negative bacteria Vibrio cholera. It is caused by the ingestion of food or water and infected all age groups.
Tsigereda Tilahun Letta +2 more
doaj +1 more source
Laplace approximation for conditional autoregressive models for spatial data of diseases
MethodsX, 2022 Conditional autoregressive (CAR) distributions are used to account for spatial autocorrelation in small areal or lattice data to assess the spatial risks of diseases.
Guiming Wang
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Spatial modelling of agro-ecologically significant grassland species using the INLA-SPDE approach
Scientific Reports, 2023 The use of spatially referenced data in agricultural systems modelling has grown in recent decades, however, the use of spatial modelling techniques in agricultural science is limited.
Andrew Fichera +4 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
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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
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BMC Public Health, 2022 Background Overweight and obesity are one of the most significant risk factors of the twenty-first century related to an increased risk in the occurrence of non-communicable diseases and associated increased healthcare costs.
Robby De Pauw +5 more
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A review of R-packages for random-intercept probit regression in small clusters
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
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Mathematics, 2023 Geostatistics data in regions always have highly spatial heterogeneous, yet the regional features of the data itself cannot be ignored. In this paper, a novel latent Bayesian spatial model is proposed, which incorporates the spatial dependence of ...
Zongyuan Xia +4 more
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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