Spatial Interaction and the Statistical Analysis of Lattice Systems

By Julian Besag. 1974

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In Statistics and Probability

Citations in the corpus

Learning Generative ConvNets via Multi-grid Modeling and Sampling (2017, background): “This is a generalization of the pseudo-likelihood estimation [2], which corresponds to the case where M consists of one pixel.”
A Theory of Generative ConvNet (2016, methodology): “While the discriminative ConvNet can be considered an unfolded version of GLM, the generative ConvNet is an elaborate Markov random field (MRF) or equivalently a Gibbs distribution [2], with its potential function defined by the ConvNet features.”
Deep Energy Estimator Networks (2018, neutral): “A very early method was pseudolikelihood (Besag, 1974).”
Modeling linkage disequilibrium and identifying recombination hotspots using single-nucleotide polymorphism data. (2003, methodology): “A concrete have been to use the pseudo-likelihood (Besag 1974) based on our conditional distribution,suggestion for how to achieve this would be to augment the space of the hidden Markov model for the mosaic L pseudo( ) n k 1 (hk |H k), (7)process (described in detail in appendix a) to include both…”
Bayesian Factor Analysis for Spatially Correlated Data, With Application to Summarizing Area-Level Material Deprivation From Census Data (2004, background): “±1; : : : ; ±N /T follows N.0; ºB¡1/, where B is an N £ N matrix with f®1; : : : ; ®N g along the diagonal and ¡®i¯ij on the off-diagonal, provided that B is symmetric and positive de nite (Besag 1974; Sun et al. 1999).”
Spatial autocorrelation and the selection of simultaneous autoregressive models (2007, methodology): “Although SAR and other autoregressive models have been known for decades in the statistical literature (Besag, 1974; Cliff & Ord, 1981), their application in ecology and species distribution research has been limited up to now (e.g. Jetz & Rahbek, 2002; Keitt et al. , 2002; Lichstein et al. , 2002;…”
Integrating spatial dependence into stochastic frontier analysis (2012, background): “A number of models have been developed to account for spatial dependence such as the spatial autoregression (SAR) model (Whittle 1954; Mead 1967; Besag 1974; Ord 1975; Anselin 1988), the spatial error model (SEM) and its variant the higher order contiguity model or spatial Durbin model that allows…”
A robust pairwise likelihood method for incomplete longitudinal binary data arising in clusters (2011, methodology): “The PWL method was initially considered by Besag (1974) and then discussed by Lindsay (1988) and Cox & Reid (2004) in more details.”
Texture Analysis (1993, methodology): “For example, generally newspaper print is organized in rectangular blocks and this fact is used One can also use texture segmentation methods for preprocessing document images to identify regions of interest [37,38,39].”
The Composite Marginal Likelihood (CML) Inference Approach with Applications to Discrete and Mixed Dependent Variable Models (2014, methodology): “The history of the approach may be traced back to the pseudo-likelihood approach of Besag (1974) for modeling spatial data, and has found traction in a variety of fields since, including genetics, spatial statistics, longitudinal analyses, and multivariate modeling.”
Gaussian maximum likelihood estimation for ARMA models II: Spatial processes (2006, methodology): “(See, e.g. Besag 1974.)”
A note on pseudolikelihood constructed from marginal densities (2004, methodology): “A similar situation arises in the analysis of spatial data, and Besag’s pseudolikelihood is obtained from `2 by choosing a = 1/2. Pairwise likelihood for the analysis of spatial data is discussed in Nott and Ryden (1999) and Heagerty and Lele (1998), building on unpublished work by Hjort. These pape”