Graph-based multivariate conditional autoregressive models

DOI10.1080/24754269.2019.1666242OpenAlexW2974584432WikidataQ127241472 ScholiaQ127241472MaRDI QIDQ5880009

Ye Liang

Publication date: 7 March 2023

Published in: Statistical Theory and Related Fields (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1402.2734

zbMATH Keywords

Markov random fieldgraphical modeldisease mappingreversible jumpareal dataG-Wishart distribution

Mathematics Subject Classification ID

Statistics (62-XX)

Uses Software

• spBayes

Cites Work

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• Reversible jump Markov chain Monte Carlo computation and Bayesian model determination
• Multi-dimensional multivariate Gaussian Markov random fields with application to image processing
• Towards a multidimensional approach to Bayesian disease mapping
• Efficient Gaussian graphical model determination under \(G\)-Wishart prior distributions
• An exploration of aspects of Bayesian multiple testing
• Some recent work on multivariate Gaussian Markov random fields
• Wishart distributions for decomposable graphs
• Bayesian Inference for General Gaussian Graphical Models With Application to Multivariate Lattice Data
• Generalized Hierarchical Multivariate CAR Models for Areal Data
• Bayesian analysis of matrix normal graphical models
• A Bivariate Bayes Method for Improving the Estimates of Mortality Rates With a Twofold Conditional Autoregressive Model
• Efficient parametrisations for normal linear mixed models
• Order-Free Co-Regionalized Areal Data Models with Application to Multiple-Disease Mapping
• A general modelling framework for multivariate disease mapping
• A Monte Carlo method for computing the marginal likelihood in nondecomposable Gaussian graphical models
• Proper multivariate conditional autoregressive models for spatial data analysis