Bayesian Inference for the Spatial Random Effects Model

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Bayesian Hierarchical Models With Conjugate Full-Conditional Distributions for Dependent Data From the Natural Exponential Family (2017, methodology): “…(see, e.g., Wikle and Cressie, 1999; Cressie and Johannesson, 2006; Shi and Cressie, 2007; Banerjee et al., 2008; Cressie and Johannesson, 2008; Finley et al., 2009; Katzfuss and Cressie, 2011; Cressie et al., 2010a,b; Kang and Cressie, 2011; Katzfuss and Cressie, 2012; Bradley et al., 2015a).”
Basis-Function Models in Spatial Statistics (2021, background): “One popular approach that does not require its knowledge is MCMC; see, for example, Nychka et al. (2002), Paciorek (2007), Wikle (2010), Kang & Cressie (2011), Eidsvik et al. (2012), and Zammit-Mangion & Rougier (2020) for the use of MCMC in a spatial-basis-function-model setting.”
Hierarchical statistical modeling of big spatial datasets using the exponential family of distributions (2013, methodology): “Following Kang and Cressie (2011), the prior distribution of θ = (β,K ,σ2ξ) is assumed to be made up of mutually independent components: [β,K ,σ2ξ] = [β] · [K ] · [σ 2 ξ].”