Approximating Hidden Gaussian Markov Random Fields
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Publication:4670799
DOI10.1111/j.1467-9868.2004.B5590.xzbMath1068.62098OpenAlexW2095691151MaRDI QIDQ4670799
Ingelin Steinsland, Sveinung Erland, Håvard Rue
Publication date: 22 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/j.1467-9868.2004.b5590.x
Random fields (60G60) Random fields; image analysis (62M40) Markov processes: estimation; hidden Markov models (62M05)
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Cites Work
- Unnamed Item
- Fast Sampling of Gaussian Markov Random Fields
- Bayesian forecasting and dynamic models.
- Space-varying regression models: specifications and simulation
- Multi-dimensional multivariate Gaussian Markov random fields with application to image processing
- Bayesian image restoration, with two applications in spatial statistics (with discussion)
- Rates of convergence of the Hastings and Metropolis algorithms
- Sequential Monte Carlo Methods in Practice
- Bayesian Prediction of Spatial Count Data Using Generalized Linear Mixed Models
- Markov chain Monte Carlo for dynamic generalised linear models
- Automatic Bayesian Curve Fitting
- Model-Based Geostatistics
- Non-parametric Bayesian Estimation of a Spatial Poisson Intensity
- Conditional Prior Proposals in Dynamic Models
- Likelihood analysis of non-Gaussian measurement time series
- Monte Carlo maximum likelihood estimation for non-Gaussian state space models
- Fitting Gaussian Markov Random Fields to Gaussian Fields
- A sequential particle filter method for static models
- Empirical supremum rejection sampling
- On Block Updating in Markov Random Field Models for Disease Mapping
- Bayesian Detection of Clusters and Discontinuities in Disease Maps
- Modelling Spatially Correlated Data via Mixtures: A Bayesian Approach
- Bayesian Analysis of Agricultural Field Experiments
- Time Series Analysis of Non-Gaussian Observations Based on State Space Models from Both Classical and Bayesian Perspectives
- Statistics for Spatial Data
- Bayesian semiparametric regression analysis of multicategorical time-space data