Modelling Spatially Correlated Data via Mixtures: A Bayesian Approach
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Publication:4672163
DOI10.1111/1467-9868.00362zbMath1067.62029OpenAlexW2061115813MaRDI QIDQ4672163
Carmen Fernández, Peter J. Green
Publication date: 29 April 2005
Published in: Journal of the Royal Statistical Society Series B: Statistical Methodology (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1111/1467-9868.00362
Poisson mixturesDisease mappingGrouped continuous modelLogistic normal modelReversible jump Markov chain Monte Carlo method
Applications of statistics to biology and medical sciences; meta analysis (62P10) Bayesian inference (62F15) Numerical analysis or methods applied to Markov chains (65C40)
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Cites Work
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- Fast Sampling of Gaussian Markov Random Fields
- Hidden Markov Models and Disease Mapping
- Bayesian Detection of Clusters and Discontinuities in Disease Maps
- Mixture Models in Measurement Error Problems, with Reference to Epidemiological Studies
- Markov chain Monte Carlo Estimation of Classical and Dynamic Switching and Mixture Models
- Bayesian Inference in Hidden Markov Models Through the Reversible Jump Markov Chain Monte Carlo Method
