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On computational aspects of Bayesian spatial models: influence of the neighboring structure in the efficiency of MCMC algorithms

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  • Vinicius Mayrink

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  • Dani Gamerman

    ()

Abstract

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Suggested Citation

  • Vinicius Mayrink & Dani Gamerman, 2009. "On computational aspects of Bayesian spatial models: influence of the neighboring structure in the efficiency of MCMC algorithms," Computational Statistics, Springer, vol. 24(4), pages 641-669, December.
  • Handle: RePEc:spr:compst:v:24:y:2009:i:4:p:641-669
    DOI: 10.1007/s00180-009-0153-0
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    References listed on IDEAS

    as
    1. Ferreira, Marco A.R. & De Oliveira, Victor, 2007. "Bayesian reference analysis for Gaussian Markov random fields," Journal of Multivariate Analysis, Elsevier, vol. 98(4), pages 789-812, April.
    2. Gamerman, Dani & Moreira, Ajax R. B. & Rue, Havard, 2003. "Space-varying regression models: specifications and simulation," Computational Statistics & Data Analysis, Elsevier, vol. 42(3), pages 513-533, March.
    3. Håvard Rue & Ingelin Steinsland & Sveinung Erland, 2004. "Approximating hidden Gaussian Markov random fields," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(4), pages 877-892.
    4. Carmen Fernández & Peter J. Green, 2002. "Modelling spatially correlated data via mixtures: a Bayesian approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 805-826.
    5. Leonhard Knorr-Held, 2002. "On Block Updating in Markov Random Field Models for Disease Mapping," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 29(4), pages 597-614.
    6. Leonhard Knorr-Held, 1999. "Conditional Prior Proposals in Dynamic Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 26(1), pages 129-144.
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