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參考文獻 [1] Besag, J. E. (1974). Spatial interaction and the statistical analysis of lattice systems. Journal of the Royal Statistical Society B, 36, 192-225. [2] Besag, J. E., York, J., and Mollie, A. (1991). Bayesian image restoration, with two applications in spatial statistics (with discussion). Annals of the Institute of Statistical mathematics, 43, 1-59. [3] Cressie, N. A. C. (1993). Statistics for spatial data, revised edition. New York: Wiley. [4] Chang, M. L., Lin ,P. S., and Tsou, H. H. (2008). Application of hierarchical models for detection of spatially clustered disease in Taiwan. Journal of the Chinese Statistical Association, 46, 22-35. [5] Chib, S., and Greenberg, E. (1995). Understanding the Metropolis Hastings algorithm. American Statistical Journal, 49, 327-335. [6] Christensen, O. F., and Waagepetersen, R. (2002). Bayesian prediction of spatial count data using generalized linear mixed models. Biometrics, 58, 280-286. [7] Geman, S. and Geman, D. (1984). Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis Machine Intelligence, 6, 721-741. [8] Hastings, W. K. (1970). Monte Carlo sampling methods using Markov chains and their applications. Biometrika, 57, 97-109. [9] Kelsalll, J. and Wakefield, J. (2002). Modeling spatial variation in disease risk: a geostatistical approach. Journal of the American Statistical Association, 97, 692-701. [10] Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., and Teller, E. (1953). Equations of state calculations by fast computing machines. Journal of Chemical Physics, 21, 1087-1092. [11] Nobre, A. A., Schmidt, A. M., and Lopes, H. F. (2005). Spatio-temporal models for mapping the incidence of malaria in Para. Environmetrics, 16, 291-304. [12] Roberts, G. O., Gelman, A., and Gilks, W. R. (1997). Weak convergence and optimal scaling of random walk metropolis algorithms. The Annals of Applied probability, 7, 110-120.
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