Spatial Statistics and Bayesian Computation

By Julian Besag, Peter J. Green. 1993

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Citations in the corpus

Control functionals for Monte Carlo integration (2014, background): “The marginalization of hyperparameters is a common problem in spatial statistics and Bayesian statistics in general (Besag and Green, 1993; Agapiou et al., 2014; Filippone and Girolami, 2014).”
Determinantal Point Processes for Machine Learning (2012, methodology): “…pseudolikelihood [Besag, 1977, Besag et al., 1982, Jensen and Moller, 1991, Ripley, 1991], Markov chain Monte Carlo methods [Ripley and Kelly, 1977, Besag and Green, 1993, Häggström et al., 1999, Berthelsen and Møller, 2006], and other approximations [Ogata and Tanemura, 1985, Diggle et al., 1994].”
General design Bayesian generalized linear mixed models (2006, background): “Clever strategies such as adaptive rejection sampling (Gilks and Wild, 1992) and slice sampling (e.g., Besag and Green, 1993; Neal, 2003) are required to draw samples.”
Weak convergence of Metropolis algorithms for non-i.i.d. target distributions (2007, unknown): “Informal guidelines for the optimal scaling problem have been proposed by, among others, [3] and [4], but the first theoretical results were obtained by [11].”
Adaptive Rejection Metropolis Sampling Within Gibbs Sampling (1995, methodology): “Indeed, more efficient Markov chain simulation can sometimes be obtained with proposal distributions which differ substantially from full conditionals (Besag and Green, 1993).”
Model‐based geostatistics (2007, methodology): “In Section 4, we attack this problem by using MCMC methods (Smith and Roberts, 1993; Besag and Green, 1993; Gilks et al., 1993), and adopting a Bayesian approach to inference.”
The Art of Data Augmentation (2001, methodology): “The use of the auxiliary variable u effectively eliminates such interactions and thus reduces the strong autocorrelation in the MCMC draws, as discussed by Besag and Green (1993), Green (1997), and Higdon (1998). The success of the Swendsen–Wang algorithm has stimulated much interest in the general ”
Issues in Bayesian Analysis of Neural Network Models (1998, background): “Also, Besag and Green (1993), albeit in a different application context, dwell on the special care required when using MCMC in multimodal problems.”
Partially Collapsed Gibbs Samplers: Illustrations and Applications (2009, background): “In the context of Gibbs sampling, it is well known that blocking or grouping steps (Liu, Wong, and Kong 1994), nesting steps (van Dyk 2000b), collapsing or marginalizing parameters (Liu 1994; Meng and van Dyk 1999), incorporating auxiliary variables (Besag and Green 1993), certain parameter transfor”
Adaptively Scaling the Metropolis Algorithm Using Expected Squared Jumped Distance (2007, background): “Alternative measures of eﬃciency in the MCMC literature are related to the eigenvalue structure of the transition kernel (see, for example, Besag and Green (1993)).”
Bayesian Analysis of Poisson Mixtures (2002, methodology): “In the normal mixture case, Richardson and Green [4] defined updating mechanisms based on the intuitive idea of exploiting the current state of the chain, i.e. proposing only local changes to few components.”
Inference from Iterative Simulation Using Multiple Sequences (1992, methodology): “…review the problems of inference from iterative simulation, we do not discuss the vast and expanding variety of simulation methods themselves , which have been described in several recent review articles (e.g., Tierney, 1991; Gelman, 1992; Besag and Green, 1993; and Smith and Roberts, 1993).”
Perfect slice samplers (2001, unknown): “" , "%&&3)) # B #6# % B B " 7) , A / $ "%&&0) " < /) % 7 8 #6# < / B B B B B B #6# " ) " ) 9 #6# # 4 " ) " # > ) @ % B % % % % % % % % B % % % % % % B % B % B B % B % B % B 5 3 % B 3 % B % % : 3 3 / $ "%&&0) % 3 B B B B B E " !”