Pages that link to "Item:Q68580"
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The following pages link to An Explicit Link between Gaussian Fields and Gaussian Markov Random Fields: The Stochastic Partial Differential Equation Approach (Q68580):
Displaying 50 items.
- A TMB approach to study spatial variation in weather-generated claims in insurance (Q6063793) (← links)
- Small Area Estimation for Disease Prevalence Mapping (Q6064364) (← links)
- A Review of Spatial Causal Inference Methods for Environmental and Epidemiological Applications (Q6067151) (← links)
- Bayesian nonstationary spatial modeling for very large datasets (Q6069068) (← links)
- Optimal spatial design for air quality measurement surveys (Q6069108) (← links)
- Bayesian inversion with α-stable priors (Q6070748) (← links)
- An adaptive sampling augmented Lagrangian method for stochastic optimization with deterministic constraints (Q6072951) (← links)
- A Bayesian Approach to Modeling Biological Pattern Formation with Limited Data (Q6074542) (← links)
- Conditional intensity: A powerful tool for modelling and analysing point process data (Q6075101) (← links)
- Galerkin-Chebyshev approximation of Gaussian random fields on compact Riemannian manifolds (Q6082219) (← links)
- Modeling Temporally Evolving and Spatially Globally Dependent Data (Q6086581) (← links)
- Geostatistical Methods for Disease Mapping and Visualisation Using Data from Spatio‐temporally Referenced Prevalence Surveys (Q6086602) (← links)
- A Statistical Review of Template Model Builder: A Flexible Tool for Spatial Modelling (Q6089895) (← links)
- Resolving the Antarctic contribution to sea‐level rise: a hierarchical modelling framework (Q6090033) (← links)
- Approximate Bayesian inference for multivariate point pattern analysis in disease mapping (Q6091727) (← links)
- Multi-output multilevel best linear unbiased estimators via semidefinite programming (Q6099233) (← links)
- Gradient boosting with extreme-value theory for wildfire prediction (Q6100555) (← links)
- A combined statistical and machine learning approach for spatial prediction of extreme wildfire frequencies and sizes (Q6100557) (← links)
- Joint modeling and prediction of massive spatio-temporal wildfire count and burnt area data with the INLA-SPDE approach (Q6100559) (← links)
- Estimating the parameters of some common Gaussian random fields with nugget under fixed-domain asymptotics (Q6103257) (← links)
- Linear-Cost Covariance Functions for Gaussian Random Fields (Q6107197) (← links)
- Uncertainty Quantification of Inclusion Boundaries in the Context of X-Ray Tomography (Q6109154) (← links)
- Deep Compositional Spatial Models (Q6110701) (← links)
- Distributed Bayesian inference in massive spatial data (Q6111472) (← links)
- A new avenue for Bayesian inference with INLA (Q6113748) (← links)
- Spatial 3D Matérn priors for fast whole-brain fMRI analysis (Q6117931) (← links)
- Improving multilevel regression and poststratification with structured priors (Q6120423) (← links)
- Gaussian orthogonal latent factor processes for large incomplete matrices of correlated data (Q6121983) (← links)
- A new construction of covariance functions for Gaussian random fields (Q6123502) (← links)
- A Spatial Variance‐Smoothing Area Level Model for Small Area Estimation of Demographic Rates (Q6131431) (← links)
- Confidence regions for the level curves of spatial data (Q6139090) (← links)
- Multivariate spatio‐temporal modelling for assessing Antarctica's present‐day contribution to sea‐level rise (Q6139134) (← links)
- Restricted spatial regression in practice: geostatistical models, confounding, and robustness under model misspecification (Q6139143) (← links)
- Regression‐based covariance functions for nonstationary spatial modeling (Q6139146) (← links)
- Computationally efficient spatial modeling of annual maximum 24‐h precipitation on a fine grid (Q6139151) (← links)
- A modeler's guide to extreme value software (Q6144812) (← links)
- High-dimensional modeling of spatial and spatio-temporal conditional extremes using INLA and Gaussian Markov random fields (Q6144814) (← links)
- A Bayesian approach for consistent reconstruction of inclusions (Q6149891) (← links)
- Optimization on Manifolds via Graph Gaussian Processes (Q6151665) (← links)
- Hierarchical off-diagonal low-rank approximation of Hessians in inverse problems, with application to ice sheet model initialization (Q6166000) (← links)
- On coregionalized multivariate Gaussian Markov random fields: construction, parameterization, and Bayesian estimation and inference (Q6169913) (← links)
- Fast Bayesian inference of block nearest neighbor Gaussian models for large data (Q6171799) (← links)
- Scalable computations for nonstationary Gaussian processes (Q6173565) (← links)
- Scalable Physics-Based Maximum Likelihood Estimation Using Hierarchical Matrices (Q6177922) (← links)
- Log-Gaussian Cox process modeling of large spatial lightning data using spectral and Laplace approximations (Q6179115) (← links)
- Credible regions for exceedance sets of geostatistical data (Q6179607) (← links)
- Practical likelihood analysis for spatial generalized linear mixed models (Q6179616) (← links)
- Variogram calculations for random fields on regular lattices using quadrature methods (Q6179742) (← links)
- Gaussian process regression in the flat limit (Q6183872) (← links)
- A scalable framework for multi-objective PDE-constrained design of building insulation under uncertainty (Q6185171) (← links)
