Spatial modeling with R‐INLA: A review
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 …
can be challenging. Writing fast inference code for a complex spatial model with realistically …
Species distribution modeling: a statistical review with focus in spatio-temporal issues
The use of complex statistical models has recently increased substantially in the context of
species distribution behavior. This complexity has made the inferential and predictive …
species distribution behavior. This complexity has made the inferential and predictive …
[图书][B] Advanced spatial modeling with stochastic partial differential equations using R and INLA
Modeling spatial and spatio-temporal continuous processes is an important and challenging
problem in spatial statistics. Advanced Spatial Modeling with Stochastic Partial Differential …
problem in spatial statistics. Advanced Spatial Modeling with Stochastic Partial Differential …
The SPDE approach for Gaussian and non-Gaussian fields: 10 years and still running
Gaussian processes and random fields have a long history, covering multiple approaches to
representing spatial and spatio-temporal dependence structures, such as covariance …
representing spatial and spatio-temporal dependence structures, such as covariance …
Constructing priors that penalize the complexity of Gaussian random fields
Priors are important for achieving proper posteriors with physically meaningful covariance
structures for Gaussian random fields (GRFs) since the likelihood typically only provides …
structures for Gaussian random fields (GRFs) since the likelihood typically only provides …
Plasmodium falciparum parasite prevalence in East Africa: Updating data for malaria stratification
VA Alegana, PM Macharia, S Muchiri… - PLOS global public …, 2021 - journals.plos.org
The High Burden High Impact (HBHI) strategy for malaria encourages countries to use
multiple sources of available data to define the sub-national vulnerabilities to malaria risk …
multiple sources of available data to define the sub-national vulnerabilities to malaria risk …
Does non-stationary spatial data always require non-stationary random fields?
A stationary spatial model is an idealization and we expect that the true dependence
structures of physical phenomena are spatially varying, but how should we handle this non …
structures of physical phenomena are spatially varying, but how should we handle this non …
Exploring a new class of non-stationary spatial Gaussian random fields with varying local anisotropy
Gaussian random fields (GRFs) play an important part in spatial modelling, but can be
computationally infeasible for general covariance structures. An efficient approach is to …
computationally infeasible for general covariance structures. An efficient approach is to …
Regression‐based covariance functions for nonstationary spatial modeling
In many environmental applications involving spatially‐referenced data, limitations on the
number and locations of observations motivate the need for practical and efficient models for …
number and locations of observations motivate the need for practical and efficient models for …
Source apportionment and spatial distribution of potentially toxic elements in soils: A new exploration on receptor and geostatistical models
Z Wang, X Chen, D Yu, L Zhang, J Wang… - Science of the Total …, 2021 - Elsevier
Potentially toxic element (PTE) pollution is considered as the main soil environmental
problem in the world. Source apportionment and spatial pattern of soil PTEs are essential for …
problem in the world. Source apportionment and spatial pattern of soil PTEs are essential for …