Efficient Algorithms for Bayesian Nearest Neighbor Gaussian Processes
From MaRDI portal
Publication:104464
DOI10.1080/10618600.2018.1537924OpenAlexW2795443545WikidataQ90205970 ScholiaQ90205970MaRDI QIDQ104464
Hans E. Andersen, Sudipto Banerjee, Abhirup Datta, Andrew Finley, Douglas C. Morton, Bruce D. Cook, Douglas C. Morton, Abhirup Datta, Hans-Erik Andersen, Sudipto Banerjee, Bruce D. Cook, Andrew O. Finley
Publication date: 1 April 2019
Published in: Journal of Computational and Graphical Statistics (Search for Journal in Brave)
Full work available at URL: https://europepmc.org/articles/pmc6753955
stochastic processesspatial analysisBayesian methodsstatistical computingcomputationally intensive methods
Related Items
Bayesian inference for high-dimensional nonstationary Gaussian processes, Parallel cross-validation: a scalable fitting method for Gaussian process models, Conjugate Bayesian Regression Models for Massive Geostatistical Data Sets, Bayesian functional registration of fMRI activation maps, Scaled Vecchia Approximation for Fast Computer-Model Emulation, Distributed nearest-neighbor Gaussian processes, Random Forests for Spatially Dependent Data, Distributed Bayesian inference in massive spatial data, Bayesian hierarchical modeling and analysis for actigraph data from wearable devices, A dynamic spatial filtering approach to mitigate underestimation bias in field calibrated low-cost sensor air pollution data, Nearest-neighbor mixture models for non-Gaussian spatial processes, Highly Scalable Bayesian Geostatistical Modeling via Meshed Gaussian Processes on Partitioned Domains, Multi-scale process modelling and distributed computation for spatial data, A general framework for Vecchia approximations of Gaussian processes, Scalable inference for space‐time Gaussian Cox processes, Unnamed Item, Spatiotemporal lagged models for variable rate irrigation in agriculture, Vecchia-approximated Deep Gaussian Processes for Computer Experiments, A case study competition among methods for analyzing large spatial data, spNNGP, A hierarchical multivariate spatio-temporal model for clustered climate data with annual cycles, Improving performances of MCMC for nearest neighbor Gaussian process models with full data augmentation, Bayesian inference for brain activity from functional magnetic resonance imaging collected at two spatial resolutions, Discussion on competition on spatial statistics for large datasets
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Covariance approximation for large multivariate spatial data sets with an application to multiple climate model errors
- Improving the performance of predictive process modeling for large datasets
- High-dimensional Bayesian geostatistics
- Inference from iterative simulation using multiple sequences
- Nonseparable dynamic nearest neighbor Gaussian process models for large spatio-temporal data with an application to particulate matter analysis
- Minimizing the Profile of a Symmetric Matrix
- Hierarchical Low Rank Approximation of Likelihoods for Large Spatial Datasets
- Direct Methods for Sparse Linear Systems
- Fixed Rank Kriging for Very Large Spatial Data Sets
- Gaussian Predictive Process Models for Large Spatial Data Sets
- A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs
- Covariance structure of the Gibbs sampler with applications to the comparisons of estimators and augmentation schemes
- Approximating Likelihoods for Large Spatial Data Sets
- An Approximate Minimum Degree Ordering Algorithm
- Strictly Proper Scoring Rules, Prediction, and Estimation
- Algorithm 837
- Inconsistent Estimation and Asymptotically Equal Interpolations in Model-Based Geostatistics