Results 21 to 30 of about 20,297 (180)
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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Discrete versus continuous domain models for disease mapping [PDF]
, 2019 The main goal of disease mapping is to estimate disease risk and identify high-risk areas. Such analyses are hampered by the limited geographical resolution of the available data.
Konstantinoudis, Garyfallos +3 more
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Molecular detection of Listeria monocytogenes from aborted women [PDF]
Journal of Bioscience and Applied ResearchAbortion, an involuntary and spontaneous termination of pregnancy, can be influenced by various factors, including potentially unknown ones. Bacterial infections play a significant role in some cases.
Rahma majid Kamel +4 more
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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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Modeling and Estimation for Self-Exciting Spatio-Temporal Models of Terrorist Activity [PDF]
, 2017 Spatio-temporal hierarchical modeling is an extremely attractive way to model the spread of crime or terrorism data over a given region, especially when the observations are counts and must be modeled discretely.
Clark, Nicholas +2 more
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Bayesian Computing with INLA: A Review [PDF]
Annual Review of Statistics and Its Application, 2017 The key operation in Bayesian inference is to compute high-dimensional integrals. An old approximate technique is the Laplace method or approximation, which dates back to Pierre-Simon Laplace (1774). This simple idea approximates the integrand with a second-order Taylor expansion around the mode and computes the integral analytically.
Rue, Håvard +5 more
openaire +4 more sources
Estimating Animal Abundance with N-Mixture Models Using the R-INLA Package for R
Journal of Statistical Software, 2020 Successful management of wildlife populations requires accurate estimates of abundance. Abundance estimates can be confounded by imperfect detection during wildlife surveys.
Timothy D. Meehan +2 more
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TMB: Automatic Differentiation and Laplace Approximation [PDF]
, 2015 TMB is an open source R package that enables quick implementation of complex nonlinear random effect (latent variable) models in a manner similar to the established AD Model Builder package (ADMB, admb-project.org).
Bell, Brad +4 more
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Bayesian computing with INLA: New features [PDF]
Computational Statistics & Data Analysis, 2013 The INLA approach for approximate Bayesian inference for latent Gaussian models has been shown to give fast and accurate estimates of posterior marginals and also to be a valuable tool in practice via the R-package R-INLA. In this paper we formalize new developments in the R-INLA package and show how these features greatly extend the scope of models ...
Martins, Thiago G. +3 more
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Computational and Structural Biotechnology Journal, 2013 We report a study of the interaction between internalin A (inlA) and human or murine E-cadherin (Ecad). inlA is used by Listeria monocytogenes to internalize itself into host cell, but the bacterium is unable to invade murine cells, which has been ...
Samuel Genheden, Leif A Eriksson
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