ArticleBiometrika2022
Graphical Gaussian Process Models for Highly Multivariate Spatial Data.
Article in Biometrika, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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Who cites it
10 citing papers in PubMed.
- Bayesian Inference for Spatial-Temporal Non-Gaussian Data Using Predictive Stacking.Bayesian analysis · 2026Article
- Graph-constrained Analysis for Multivariate Functional Data.Journal of multivariate analysis · 2025Article
- Dynamic Bayesian Learning for Spatiotemporal Mechanistic Models.Journal of machine learning research : JMLR · 2025Article
- Bayesian Multi-Group Gaussian Process Models for Heterogeneous Group-Structured Data.Journal of machine learning research : JMLR · 2025Article
- Neural networks for geospatial data.Journal of the American Statistical Association · 2025Article
- Spatial meshing for general Bayesian multivariate models.Journal of machine learning research : JMLR · 2024Article
- Graphical Dirichlet Process for Clustering Non-Exchangeable Grouped Data.Journal of machine learning research : JMLR · 2024Article
- Modeling Multivariate Spatial Dependencies Using Graphical Models.The New England Journal of Statistics in Data Science · 2023Article
- Detecting clusters of transcription factors based on a nonhomogeneous poisson process model.BMC bioinformatics · 2022Article
- Hierarchical multivariate directed acyclic graph autoregressive models for spatial diseases mapping.Statistics in medicine · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
For multivariate spatial Gaussian process (GP) models, customary specifications of cross-covariance functions do not exploit relational inter-variable graphs to ensure process-level conditional independence among the variables. This is undesirable, especially for highly multivariate settings, where popular cross-covariance functions such as the multivariate Matérn suffer from a "curse of dimensionality" as the number of parameters and floating point operations scale up in quadratic and cubic order, respectively, in the number of variables. We propose a class of multivariate "Graphical Gaussian Processes" using a general construction called "stitching" that crafts cross-covariance functions from graphs and ensures process-level conditional independence among variables. For the Matérn family of functions, stitching yields a multivariate GP whose univariate components are Matérn GPs, and conforms to process-level conditional independence as specified by the graphical model. For highly multivariate settings and decomposable graphical models, stitching offers massive computational gains and parameter dimension reduction. We demonstrate the utility of the graphical Matérn GP to jointly model highly multivariate spatial data using simulation examples and an application to air-pollution modelling.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.