[10] Ferreira, M. A. R., Higdon, D., Lee, H. K. H., West, M. (2005) Multi-scale random
field models. Technical Report 186, Departamento de m´etodos estat´ısticos, Univer-
sidade Federal do Rio de Janeiro, Rio de Janeiro.
[11] Ferreira, M. A. R., Oliveira, V. (2007) Bayesian reference analysis for Gaussian
Markov random fields. Journal of Multivariate Analysis, to appear.
[12] Fr¨uhwirth-Schnatter, S. (1994) Data augmentation and dynamic linear models. Jour-
nal of Time Series Analysis, 15, 183-202.
[13] Gamerman, D., Lopes, H. F. (2006) Markov Chain Monte Carlo: Stochastic Simu-
lation for Bayesian Inference, Chapman & Hall/CRC, London.
[14] Gamerman, D., Moreira, A. R. B., Rue, H. (2003) Space varying regression models:
specifications and simulation. Computational Statistics & Data Analysis, 42, 513-
533.
[15] Geman, S., Geman, D. (1984) Stochastic relaxation, gibbs distributions and the
Baye sian restoration of imagens. IEEE Transactions on Patterns Analysis and Ma-
chine Intelligence, 6, 721-741.
[16] Gelfand, A. E., Smith, A. F. M. (1990) Sampling-based approaches to calculating
marginal densities. Journal of the American Statistical Association, 85, 398-409.
[17] Gelfand, A. E., Vounatsou, P. (2001) Proper multivariate conditional autoregressive
models for spatial data analysis, Mimeo, Research Report 301-315, University of
Connecticut.
[18] Hastings, W. K. (1970) Monte carlo sampling methods using markov chains and
their applications. Biometrika, 57 , 97-109.
[19] Knorr-Held, L. (1999) Conditional prior proposals in dynamic models. Scandinavian
Journal of Statistics, Blackwell Publishers Ltd., 26, 129-144.
[20] Knorr-Held, L., Raber, G., Becker, N. (2002) Disease mapping of stage-specific
cancer incidence data. Biometrics, 58, 492-501.
132