A two-stage Bayesian modelling framework with applications in spatial epidemiology [PDF]
This thesis proposes a framework for doing two-stage modelling in spatial epidemiology, whose main goal is to understand the association between a covariate of interest, which is modelled in the first stage, and health outcomes, which is modelled in the ...
Villejo, Stephen Jun Vecera
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We propose a spatio-temporal data-fusion framework for point data and gridded data with variables observed on different spatial supports. A latent Gaussian field with a Matérn-SPDE prior provides a continuous space representation, while source-specific observation operators map observations to both point measurements and gridded averages, addressing ...
Zheng, Weiyue +3 more
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Improving Earthquake Disaster Models with Post-Event Data: Insights from the 2015 Gorkha, Nepal Earthquake [PDF]
, 2019 Immense amounts of data are collected following earthquake disasters. Yet, it remains unclear how researchers’ might take full advantage of diverse post-disaster datasets. Using data from the 2015 Gorkha Nepal earthquake, this dissertation explores three
Wilson, Bradley Steven
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Correction to: A Bayesian Model for Estimating the Effects of Human Disturbance on Wildlife Habitats Based on Nighttime Light Data and INLA‑SPDE [PDF]
Applied Spatial Analysis and Policy, 2022 Changbai Xi +4 more
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Spatial models for probabilistic prediction of wind power with application to annual-average and high temporal resolution data [PDF]
, 2016 Clemmensen, Line Katrine Harder +3 more
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A comparison of extreme gradient and Gaussian process boosting for a spatial logistic regression on satellite data [PDF]
The paper compares two advanced boosting techniques, Extreme Gradient Boosting (XGBoost) and Gaussian Process Boosting (GPBoost), for spatial logistic regression models using satellite data.
Renfrew, Michael, Worton, Bruce J
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Spatial data fusion adjusting for preferential sampling using INLA and SPDE
Spatially misaligned data can be fused by using a Bayesian melding model that assumes that underlying all observations there is a spatially continuous Gaussian random field process.
Amaral, André Victor Ribeiro +2 more
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The impact of choosing different meshes under INLA/SPDE framework for geostatistical modelling [PDF]
, 2018 openaire +1 more source