Model nonstationary spatial processes using normalizing flows.
problem Difficult selection of spatial warping functions.
method Neural autoregressive flows (NAFs) for complex, high-dimensional warpings.
result NAFs model has greater representational capacity than other spatial process models.
Proposes flexible spatial models for better understanding spatial heterogeneity.
problem Poor characterisation of spatial heterogeneity in conventional models.
method Spatial Bayesian Neural Networks (SBNNs) incorporating a spatial embedding layer and possibly spatially-varying parameters.
result SBNNs better match the finite-dimensional distribution of target spatial processes.
NCS enables efficient and accurate conditional simulation for complex spatial processes.
problem Challenges in simulating from complex spatial process distributions.
method Neural diffusion models and conditional score-based diffusion.
result NCS outperforms traditional methods in efficiency and accuracy.
EFDM models spatial point processes with variable cardinality using existence variables.
problem Challenges in extending diffusion models to variable-cardinality spatial point processes.
method Existence-field diffusion model (EFDM) that jointly models spatial locations and cardinality without discrete transitions.
result EFDM achieves improved modeling capability on datasets with varying cardinality.
Paper develops a method to predict spatial point processes with guarantees.
problem Predicting the number of events in space with uncertainty.
method Regularized method to learn spatial models with out-of-sample guarantees.
result Method provides valid prediction intervals even when model is misspecified.
Neural networks estimate spatial process likelihoods efficiently.
problem Challenges in estimating spatial processes with slow or intractable likelihoods.
method Convolutional neural networks trained on a classification task to learn likelihood function.
result Neural likelihood surfaces provide fast and accurate parameter estimation.
We propose a probabilistic model for refining coarse-grained spatial data by utilizing auxiliary spatial data sets. Existing methods require that the spatial granularities of the auxiliary data sets are the same as the desired granularity of target data. The proposed model can effectively make use of auxiliary data set…
A scalable Bayesian linear regression framework for spatial data.
problem Scalable methodologies for analyzing large spatial datasets.
method Conjugate Bayesian linear regression framework.
result Exact sampling from joint posterior distribution without iterative algorithms.
Develops statistical methods for rates of change on Riemannian manifolds.
problem Statistical inference for rates of change in spatial processes over non-Euclidean domains.
method Formalizes smoothness and constructs differential processes for Riemannian manifolds, derives conditions for kernel existence, and develops predictive inference.
result Validates theoretical findings through simulation experiments for derivatives over polyhedral meshes.
Method selects significant spatial covariates in noisy data.
problem Identifying true spatial covariates in noisy data.
method Combines sparsity-promoting estimation with noise-robust model selection.
result Method reliably recovers true covariates under diverse noise scenarios.
ConvNets improve nonstationary covariance estimation for large-scale spatial data.
problem Estimating nonstationary spatial covariance functions on large scales.
method Convolutional Neural Networks (ConvNets) for subregion identification and selection.
result Enhanced accuracy in parameter estimation using ConvNet-based partitioning.
Novel approach uses Gaussian processes to estimate conflict trends.
problem Estimating temporal and spatial patterns of violent conflict.
method Highly disaggregated conflict event data with Gaussian processes.
result Powerful conflict forecasts and insights into conflict dynamics.
Machine learning models perform better with location coordinates alone, not Moran Eigenvectors.
problem Improving machine learning models for spatial data.
method Examined Moran Eigenvectors as additional spatial features in machine learning models using synthetic datasets.
result Machine learning models using only location coordinates achieve better accuracies than eigenvector-based approaches.
Flexible XVAE model for efficient spatial extremes simulation.
problem Complex tail dependence structures in spatial extremes processes.
method Variational autoencoder (XVAE) for modeling flexible and non-stationary dependence.
result XVAE provides fast inference and outperforms traditional models in high dimensions.
Bayesian approach models earthquake clustering with spatial mainshocks and aftershocks.
problem Estimating uncertainty in earthquake clustering models due to complex likelihood functions.
method Nonparametric Dirichlet process mixture prior for spatial mainshocks and an auxiliary latent variable routine for efficient inference.
result Efficient Bayesian forecasting of spatial earthquake occurrences with uncertainty quantification.
Combines BART and Gaussian process for spatial covariate prediction with uncertainty.
problem Improving spatial prediction models with nonlinear and interaction covariates.
method Bayesian Additive Regression Trees (BART) combined with Gaussian process for spatial dependence.
result Effective in reducing computational burden through INLA and MCMC.
New framework models complex spatial data with basis functions and graphical vectors.
problem Modeling highly-multivariate spatial processes with varying resolutions.
method Extends graphical lasso to multivariate Gaussian processes with independent graphical vectors at different resolutions, using an orthogonal basis and fusion penalty.
result Linear complexity and parsimonious conditional independence structure in multilevel graphical model.
Graph neural networks extend neural Bayes estimators to irregular spatial data.
problem Estimating parameters from irregular spatial data with computational efficiency.
method Employing graph neural networks to approximate Bayes estimators for irregular spatial data.
result Extending neural Bayes estimation to irregular spatial data with computational benefits.
Deep models predict spatial phenomena with better accuracy.
problem Nonstationary and anisotropic spatial data modeling.
method Deep compositional spatial models using deep learning and approximate Bayesian inference.
result Deep compositional models provide better predictions and uncertainty quantification.
Researchers develop a new spatial process model for non-Gaussian data.
problem Non-Gaussian spatial data with asymmetry and heavy-tailedness.
method Re-parameterized Unified Skew-Normal (SUN) distribution, GSUN process, neural Bayes inference with GATs.
result GSUN process captures non-Gaussian spatial data properties and outperforms conventional models.
We propose an efficient method for estimating covariate effects in doubly-stochastic spatial models.
problem Computational demands and restrictive assumptions in existing doubly-stochastic spatial models.
method Penalized regression method for estimating covariate effects in doubly-stochastic point processes.
result Consistency and asymptotic normality of the covariate effect estimates achieved despite model misspecification.
Spatially-aware model improves earthquake hazard assessment accuracy.
problem Misrepresentation of seismic effects across diverse landscapes.
method Causal Bayesian network with Gaussian Processes and normalizing flows.
result Achieves up to 35.2% AUC improvement over existing methods.
Proposes a new Hawkes process bandit model for disaster search and rescue.
problem Forecasting and detecting spatio-temporal events with undersampled or biased data.
method Upper confidence bound algorithm using Bayesian spatial Hawkes process estimation.
result Model outperforms state-of-the-art spatial MAB algorithms in disaster search and rescue.
Improves deep generative models to generate images of any size.
problem Fixed-sized output images from deep generative models.
method Integrates spatial noise vectors into fully convolutional neural networks.
result Theoretical interpretation of infinite spatial generation using spatial stochastic processes.
The paper develops methods to create private synthetic spatial point patterns.
problem Generating private synthetic spatial point patterns.
method Developed differentially private Poisson and Cox point synthesizers.
result The synthesizers effectively maintain privacy and utility of synthetic data.
A new model predicts financial volatility across firms using spatial correlations.
problem Predicting financial volatility across firms in a network.
method Heterogeneous spatiotemporal GARCH model with local likelihood estimation.
result The model captures spatial spillovers and contagion effects in financial networks.
Neural networks speed up covariance estimation in spatial Gaussian processes.
problem Efficiently estimating covariance parameters in spatial Gaussian processes.
method Training neural networks to approximate maximum likelihood estimates.
result Neural network estimates are as accurate as ML methods but much faster.
Diffusion Transformer captures spatial-temporal dependencies in sequential data.
problem Capturing rich spatial and temporal dependencies in sequential data.
method Established theoretical guarantees for diffusion transformers learning Gaussian process data.
result Spatial-temporal dependencies are captured within attention layers of diffusion transformers.
A new method clusters hyperspectral images using spatially regularized diffusion.
problem Clustering hyperspectral images effectively.
method Spatially regularized random walks and diffusion geometry.
result The method outperforms state-of-the-art algorithms on real data.
DCK improves air quality index prediction with probabilistic spatial models.
problem Non-Gaussian, complex spatial structure of air quality index.
method Deep classifier kriging (DCK) for non-Gaussian, nonlinear spatial prediction.
result DCK outperforms conventional methods in predictive accuracy and uncertainty quantification.
Method predicts spatial values with few data using GP framework.
problem Few data limit predictive performance in spatial regression.
method Trains neural network to infer task representation from small data, uses GP framework to predict spatial values.
result Proposed method achieves better predictive performance than meta-learning methods.
Spatial blind source separation simplifies multivariate spatial prediction.
problem Predicting multivariate measurements at unobserved locations with spatial dependencies.
method Spatial blind source separation as a pre-processing tool compared to Cokriging and neural networks.
result Spatial blind source separation simplifies spatial prediction by avoiding cross-dependencies.
Model predicts severity of traffic accidents using spatial and temporal features.
problem Estimating severity of traffic accidents in aggregated and disaggregated data.
method Gradient Boosting models and Gaussian Processes for inference and feature importance.
result Complexity of road networks and other situational features significantly impact accident severity.
The paper uses deep learning to speed up spatial and visual connectivity analysis.
problem Slow calculation of spatial and visual connectivity metrics.
method Investigates machine learning models and a pipeline for training them on spatial and visual connectivity analysis.
result Deep learning models significantly speed up the analysis process.
Normative modeling has recently been proposed as an alternative for the case-control approach in modeling heterogeneity within clinical cohorts. Normative modeling is based on single-output Gaussian process regression that provides coherent estimates of uncertainty required by the method but does not consider spatial c…
A new geostatistical model using deep learning for spatial prediction.
problem Traditional kriging assumptions and limitations.
method Nearest-Neighbor Neural Network (4N) process integrating deep learning.
result 4N process outperforms existing geostatistical methods for non-Gaussian data.
The understanding of geographical reality is a process of data representation and pattern discovery. Former studies mainly adopted continuous-field models to represent spatial variables and to investigate the underlying spatial continuity/heterogeneity in the regular spatial domain. In this article, we introduce a more…
Integrates Fourier features for faster Gaussian process regression.
problem Efficiently scaling Gaussian process regression to large datasets.
method Integrated Fourier features for Gaussian processes.
result Improves Gaussian process regression speed to O(M3) for a broad class of kernels. The paper addresses ill-conditioning in large spatial data, proposing solutions for prediction and likelihood estimation.
problem Ill-conditioning of the kernel matrix in large spatial data sets.
method Introduction of various optimality criteria and solutions for managing large spatial data.
result Solutions for managing large spatial data, addressing ill-conditioning and improving prediction and likelihood estimation.
We develop a machine learning approach to represent and analyze the underlying spatial structure that governs shot selection among professional basketball players in the NBA. Typically, NBA players are discussed and compared in an heuristic, imprecise manner that relies on unmeasured intuitions about player behavior. T…
DeepKriging uses DNNs to predict spatial data with improved accuracy and scalability.
problem Predicting spatial processes with non-linear and non-Gaussian data.
method Adds an embedding layer of spatial coordinates with basis functions to DNNs.
result DeepKriging provides non-linear predictions with smaller approximation errors and is scalable for large datasets.
We derive generalized estimators for a number of spatial statistics that have been used in the analysis of spatially resolved omics data, such as Ripley's K, H and L functions, clustering index, and degree of clustering, which allow these statistics to be calculated on data modelled by arbitrary random measures (RMs). …
Vecchia approximations provide the best accuracy-runtime trade-off for Gaussian process approximations.
problem High computational cost of Gaussian processes for large data sets.
method Systematic comparison of different Gaussian process approximations.
result Vecchia approximations consistently provide the best accuracy-runtime trade-off.
New method models precipitation extremes and spatial dependence.
problem Estimating dependencies of precipitation maxima in space and time.
method Generative neural networks for max-stable processes.
result Explicit nonparametric estimate of spatial dependence.
Graph Element Networks adaptively model spatial processes without prior graph structure.
problem Modeling spatial processes without a priori graphical structure.
method Assign nodes to spatial locations, optimize their connectivity, and use GNNs as a computational substrate.
result Optimized GNN nodes focus on complex parts of the space, allowing generalization and varying precision.
BKP R package models spatially varying binomial probabilities efficiently.
problem Modeling spatially varying binomial probabilities efficiently.
method Beta Kernel Process (BKP) combining localized kernel-weighted likelihoods with conjugate beta priors.
result Closed-form posterior inference without requiring latent variables or intensive MCMC sampling.
Develops deep models to handle nonstationary spatial extremal dependence.
problem Challenges in modeling nonstationary extremal dependence in spatial data.
method Deep compositional spatial models to capture nonstationarity in extremal dependence.
result Efficient estimation of warped space for nonstationary spatial data.
A-BLINK speeds up Gaussian process covariance estimation.
problem Slow covariance matrix inversion in Gaussian processes.
method Two pre-trained neural networks learn Kriging weights and spatial variance.
result Significant computational speedups and posterior inference.