Exact bayesian geostatistics using predictive stacking
We develop Bayesian predictive stacking for geostatistical models. Our approach builds an
augmented Bayesian linear regression framework that subsumes the realizations of the …
augmented Bayesian linear regression framework that subsumes the realizations of the …
A class of modular and flexible covariate-based covariance functions for nonstationary spatial modeling
The assumptions of stationarity and isotropy often stated over spatial processes have not
aged well during the last two decades, partly explained by the combination of computational …
aged well during the last two decades, partly explained by the combination of computational …
Which parameterization of the Matérn covariance function?
The Matérn family of covariance functions is currently the most popularly used model in
spatial statistics, geostatistics, and machine learning to specify the correlation between two …
spatial statistics, geostatistics, and machine learning to specify the correlation between two …
Default Priors for the Smoothness Parameter in Gaussian Matérn Random Fields
Z Han, V De Oliveira - Bayesian Analysis, 2024 - projecteuclid.org
The Matérn family of covariance functions plays a prominent role in the analysis of
geostatistical data due to its ability to model different smoothness behaviors. Although in …
geostatistical data due to its ability to model different smoothness behaviors. Although in …
Spatial causal inference in the presence of preferential sampling to study the impacts of marine protected areas
Marine Protected Areas (MPAs) have been established globally to conserve marine
resources. Given their maintenance costs and impact on commercial fishing, it is critical to …
resources. Given their maintenance costs and impact on commercial fishing, it is critical to …
A Fully Bayesian Extension to FEMU for Identification of Spatially Varying Elastic Properties from Digital Image and Volume Correlation Measurements
A Touminet, S Cantournet, V Fabre, P Kerfriden - 2024 - hal.science
We present a fully Bayesian framework for identifying spatially varying elastic parameters
and their covariance properties using noisy displacement observations obtained with DIC or …
and their covariance properties using noisy displacement observations obtained with DIC or …
[引用][C] Estimating the Spatial Dynamics of Plant Recruitment using Approximate Bayesian Computation
J Freeman - 2023