Results 11 to 20 of about 5,732 (193)
Predicting spatio-temporal dynamics of dengue using INLA (integrated nested laplace approximation) in Yogyakarta, Indonesia [PDF]
BMC Public HealthIntroduction Dengue is a mosquito-borne disease caused by the dengue virus, primarily transmitted by Aedes aegypti and Aedes albopictus. Its incidence fluctuates due to spatial and temporal factors, necessitating robust modeling approaches for prediction
Marko Ferdian Salim +2 more
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Unveiling disparities in lung cancer care: a joint spatio-temporal analysis of multidisciplinary meeting presentation, supportive care screening, and diagnostic timeliness in Victoria [PDF]
BMC MedicineBackground Lung cancer remains the most diagnosed malignancy and the leading cause of cancer-related mortality worldwide. Improving key clinical quality indicators (CQIs), including multidisciplinary meeting (MDM) presentation, supportive care screening,
Getayeneh Antehunegn Tesema +3 more
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Revista Latinoamericana de Población, 2020 El trabajo busca modelar la distribución de la tasa de incidencia acumulada de COVID-19 en los municipios de México a través del ajuste de tres modelos lineales generalizados (en competencia) con efectos espaciales y temporales y función de enlace ...
Gerardo Núñez Medina
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Competing risks joint models using R-INLA [PDF]
Statistical Modelling, 2020 The methodological advancements made in the field of joint models are numerous. None the less, the case of competing risks joint models has largely been neglected, especially from a practitioner's point of view. In the relevant works on competing risks joint models, the assumptions of a Gaussian linear longitudinal series and proportional cause ...
Van Niekerk, Janet +2 more
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Spatial modeling with R‐INLA: A review [PDF]
WIREs Computational Statistics, 2018 Coming up with Bayesian models for spatial data is easy, but performing inference with them can be challenging. Writing fast inference code for a complex spatial model with realistically‐sized datasets from scratch is time‐consuming, and if changes are made to the model, there is little guarantee that the code performs well.
Bakka, Haakon +8 more
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New Frontiers in Bayesian Modeling Using the INLA Package in R
Journal of Statistical Software, 2021 The INLA package provides a tool for computationally efficient Bayesian modeling and inference for various widely used models, more formally the class of latent Gaussian models.
Janet Van Niekerk +3 more
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Spatial and spatio-temporal models with R-INLA [PDF]
Spatial and Spatio-temporal Epidemiology, 2013 During the last three decades, Bayesian methods have developed greatly in the field of epidemiology. Their main challenge focusses around computation, but the advent of Markov Chain Monte Carlo methods (MCMC) and in particular of the WinBUGS software has opened the doors of Bayesian modelling to the wide research community. However model complexity and
Blangiardo, Marta +3 more
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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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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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Bayesian Multivariate Spatial Models for Lattice Data with INLA
Journal of Statistical Software, 2021 The INLAMSM package for the R programming language provides a collection of multivariate spatial models for lattice data that can be used with the INLA package for Bayesian inference.
Francisco Palmí-Perales +2 more
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