Spatial Statistics and Bayesian Computation
作者： Julian Besag , Peter J. Green
DOI: 10.1111/J.2517-6161.1993.TB01467.X
关键词: Gibbs sampling 、 Hyperparameter 、 Mathematics 、 Bayesian probability 、 Markov random field 、 Bayesian inference 、 Artificial intelligence 、 Econometrics 、 Machine learning 、 Variable-order Bayesian network 、 Bayesian statistics 、 Markov chain Monte Carlo
摘要: … Here, we briefly describe a spatial application of MCMC methods to the Bayesian analysis of agricultural field experiments. We do not discuss individual analyses but summarize our …
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sci-hub.se 参考文章(49)
Charles J. Geyer, Elizabeth A. Thompson, Constrained Monte Carlo Maximum Likelihood for Dependent Data Journal of the royal statistical society series b-methodological. ,vol. 54, pp. 657- 683 ,(1992) , 10.1111/J.2517-6161.1992.TB01443.X
S Geman, D McClure, Statistical methods for tomographic image reconstruction ,(1987)
K. V. Mardia, J. T. Kent, Statistical Shape Models in Image Analysis ,(1992)
Ulf Grenander, Michael I. Miller, Representations of Knowledge in Complex Systems Journal of the Royal Statistical Society: Series B (Methodological). ,vol. 56, pp. 549- 581 ,(1994) , 10.1111/J.2517-6161.1994.TB02000.X
Luke Tierney, Exploring Posterior Distributions Using Markov Chains ,(1992)
Charles J. Geyer, Markov Chain Monte Carlo Maximum Likelihood Interface Foundation of North America. ,(1991)
U. Grenander, Y. Chow, D. M. Keenan, Hands: A Pattern Theoretic Study of Biological Shapes ,(1990)
Peter Green, Christopher Jennison, Allan Seheult, Analysis of Field Experiments by Least Squares Smoothing Journal of the royal statistical society series b-methodological. ,vol. 47, pp. 299- 315 ,(1985) , 10.1111/J.2517-6161.1985.TB01358.X
D. Geman, Random fields and inverse problems in imaging Springer, Berlin, Heidelberg. pp. 115- 193 ,(1990) , 10.1007/BFB0103042
Ulli Wolff, Collective Monte Carlo updating for spin systems. Physical Review Letters. ,vol. 62, pp. 361- 364 ,(1989) , 10.1103/PHYSREVLETT.62.361