Pages that link to "Item:Q68580"

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.

• Stochastic PDE representation of random fields for large-scale Gaussian process regression and statistical finite element analysis (Q6187654) (← links)
• Theoretical Guarantees for the Statistical Finite Element Method (Q6188693) (← links)
• Nonparametric Bayesian modeling and estimation of spatial correlation functions for global data (Q6201430) (← links)
• Gaussian Whittle-Matérn fields on metric graphs (Q6201867) (← links)
• Derivative-informed neural operator: an efficient framework for high-dimensional parametric derivative learning (Q6202135) (← links)
• Controlling the flexibility of non-Gaussian processes through shrinkage priors (Q6203348) (← links)
• Past, present and future of software for Bayesian inference (Q6540228) (← links)
• A determinant-free method to simulate the parameters of large Gaussian fields (Q6540521) (← links)
• Spatial data fusion for large non-Gaussian remote sensing datasets (Q6540535) (← links)
• Heteroscedastic asymmetric spatial processes (Q6541480) (← links)
• Investigating mesh-based approximation methods for the normalization constant in the log Gaussian Cox process likelihood (Q6541574) (← links)
• Bayesian group learning for shot selection of professional basketball players (Q6541715) (← links)
• Point spread function approximation of high-rank Hessians with locally supported nonnegative integral kernels (Q6543105) (← links)
• A stochastic locally diffusive model with neural network-based deformations for global sea surface temperature (Q6543845) (← links)
• Covariance–Based Rational Approximations of Fractional SPDEs for Computationally Efficient Bayesian Inference (Q6552525) (← links)
• Nearest neighbors weighted composite likelihood based on pairs for (non-)Gaussian massive spatial data with an application to Tukey-\(hh\) random fields estimation (Q6554236) (← links)
• Smoothed model-assisted small area estimation of proportions (Q6554756) (← links)
• A flexible Bayesian tool for CoDa mixed models: logistic-normal distribution with Dirichlet covariance (Q6570343) (← links)
• Comparison of statistical models to predict age-standardized cancer incidence in Switzerland (Q6572276) (← links)
• Integrated nested Laplace approximations for large-scale spatiotemporal Bayesian modeling (Q6575345) (← links)
• The Matérn model: a journey through statistics, numerical analysis and machine learning (Q6579154) (← links)
• A Bayesian data modelling framework for chemical processes using adaptive sequential design with Gaussian process regression (Q6580741) (← links)
• An efficient workflow for modelling high-dimensional spatial extremes (Q6581672) (← links)
• A diffusion-based spatio-temporal extension of Gaussian Matérn fields (Q6583113) (← links)
• Microstructurally-informed stochastic inhomogeneity of material properties and material symmetries in 3D-printed 316 L stainless steel (Q6584864) (← links)
• Finite elements for Matérn-type random fields: uncertainty in computational mechanics and design optimization (Q6588299) (← links)
• Scaling priors for intrinsic Gaussian Markov random fields applied to blood pressure data (Q6590552) (← links)
• Spatio-temporal forecasting and uncertainty quantification of COVID-19 cases in Shanghai via a Bayesian deep learning approach (Q6592374) (← links)
• Extending the generalized Wendland covariance model (Q6595780) (← links)
• hIPPYlib-MUQ: a Bayesian inference software framework for integration of data with complex predictive models under uncertainty (Q6601373) (← links)
• 30 years of space-time covariance functions (Q6602109) (← links)
• Statistical challenges in estimating past climate changes (Q6602207) (← links)
• Spatial modeling with R-INLA: a review (Q6602213) (← links)
• Nearest-neighbor sparse Cholesky matrices in spatial statistics (Q6602373) (← links)
• Regularity theory for a new class of fractional parabolic stochastic evolution equations (Q6606155) (← links)
• Generalised additive point process models for natural hazard occurrence (Q6615772) (← links)
• Spatial Statistical Downscaling for Constructing High-Resolution Nature Runs in Global Observing System Simulation Experiments (Q6621646) (← links)
• Bayesian spatial models for voxel-wise prostate cancer classification using multi-parametric magnetic resonance imaging data (Q6622252) (← links)
• Resolution Adaptive Fixed Rank Kriging (Q6622421) (← links)
• Permutation and Grouping Methods for Sharpening Gaussian Process Approximations (Q6622448) (← links)
• The complexity of finding and enumerating optimal subgraphs to represent spatial correlation (Q6623585) (← links)
• Multilevel approximation of Gaussian random fields: covariance compression, estimation, and spatial prediction (Q6624469) (← links)
• Modelling imperfect presence data obtained by citizen science (Q6625855) (← links)
• Sparsity in nonlinear dynamic spatiotemporal models using implied advection (Q6625866) (← links)
• Benefits of spatiotemporal modeling for short-term wind power forecasting at both individual and aggregated levels (Q6625902) (← links)
• Remote effects spatial process models for modeling teleconnections (Q6626015) (← links)
• A large-scale spatio-temporal binomial regression model for estimating seroprevalence trends (Q6626025) (← links)
• Bayesian model -- data synthesis with an application to global glacio-isostatic adjustment (Q6626029) (← links)
• A spatio-temporal approach to estimate patterns of climate change (Q6626033) (← links)
• Axially symmetric models for global data: a journey between geostatistics and stochastic generators (Q6626034) (← links)