Results 31 to 40 of about 3,106 (192)
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
doaj +1 more source
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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Incorporating Biotic Information in Species Distribution Models: A Coregionalized Approach
Mathematics, 2021 In this work, we discuss the use of a methodological approach for modelling spatial relationships among species by means of a Bayesian spatial coregionalized model.
Xavier Barber +5 more
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A Comparison of Nonergodic Ground-Motion Models based on Geographically Weighted Regression and the Integrated Nested Laplace Approximation [PDF]
, 2021 Different nonergodic Ground-Motion Models based on spatially varying coefficient models are compared for ground-motion data in Italy. The models are based different methodologies: Multi-source geographically weighted regression (Caramenti et al., 2020 ...
Nicolas Kuehn
core +1 more source
Bayesian Analysis of Population Health Data
Mathematics, 2021 The analysis of population-wide datasets can provide insight on the health status of large populations so that public health officials can make data-driven decisions. The analysis of such datasets often requires highly parameterized models with different
Dorota Młynarczyk +3 more
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Integrated Nested Laplace Approximation as a new approximation method for the combined model: A simulation study [PDF]
, 2018 © 2017, © 2017 Taylor & Francis Group, LLC. The combined model accounts for different forms of extra-variability and has traditionally been applied in the likelihood framework, or in the Bayesian setting via Markov chain Monte Carlo.
Thomas Neyens +6 more
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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
EpiMix: A novel method to estimate effective reproduction number
Infectious Disease Modelling, 2023 Transmission potential of a pathogen, often quantified by the time-varying reproduction number Rt, provides the current pace of infection for a disease and indicates whether an emerging epidemic is under control. In this study, we proposed a novel method,
Shihui Jin +3 more
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Media Statistika, 2019 Poverty is a complex and multidimensional problem so that it becomes a development priority. Applications of poverty modeling in discrete data are still few and applications of the Bayesian paradigm are also still few.
Retsi Firda Maulina +2 more
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Integrated Nested Laplace Approximations for Large-Scale Spatial-Temporal Bayesian Modeling
CoRR, 2023 Bayesian inference tasks continue to pose a computational challenge. This especially holds for spatial-temporal modeling where high-dimensional latent parameter spaces are ubiquitous. The methodology of integrated nested Laplace approximations (INLA) provides a framework for performing Bayesian inference applicable to a large subclass of additive ...
Gaedke-Merzhäuser, Lisa +4 more
openaire +2 more sources