Multi-scale process modelling and distributed computation for spatial data
DOI10.1007/s11222-020-09962-6zbMath1452.62364arXiv1907.07813OpenAlexW3042466859MaRDI QIDQ2209724
Jonathan Rougier, Andrew Zammit-Mangion
Publication date: 4 November 2020
Published in: Statistics and Computing (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1907.07813
Directional data; spatial statistics (62H11) Computational methods for problems pertaining to statistics (62-08) Inference from stochastic processes and prediction (62M20) Applications of statistics to environmental and related topics (62P12) Monte Carlo methods (65C05) Coloring of graphs and hypergraphs (05C15)
Uses Software
Cites Work
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- An Explicit Link between Gaussian Fields and Gaussian Markov Random Fields: The Stochastic Partial Differential Equation Approach
- Efficient Algorithms for Bayesian Nearest Neighbor Gaussian Processes
- Parallel inference for massive distributed spatial data using low-rank models
- Blind source separation for spatial compositional data
- Parameter estimation in high dimensional Gaussian distributions
- Bayesian computation and stochastic systems. With comments and reply.
- Posterior inference for sparse hierarchical non-stationary models
- Going off grid: computationally efficient inference for log-Gaussian Cox processes
- Fixed Rank Kriging for Very Large Spatial Data Sets
- Gaussian Predictive Process Models for Large Spatial Data Sets
- Partially Collapsed Gibbs Samplers
- A theoretical analysis of backtracking in the graph coloring problem
- Fitting Gaussian Markov Random Fields to Gaussian Fields
- On Block Updating in Markov Random Field Models for Disease Mapping
- Data-Driven Spatio-Temporal Modeling Using the Integro-Difference Equation
- Variational Estimation in Spatiotemporal Systems From Continuous and Point-Process Observations
- A Full Scale Approximation of Covariance Functions for Large Spatial Data Sets
- Gaussian Markov Random Fields
- Sampling Strategies for Fast Updating of Gaussian Markov Random Fields
- Computational Physics
- Strictly Proper Scoring Rules, Prediction, and Estimation
- A Bayesian Kriged Kalman Model for Short-Term Forecasting of Air Pollution Levels
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