Modelling spatially correlated data via mixtures: a Bayesian approach

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Generalized spatial dirichlet process models (2007, methodology): “Flexible and computationally tractable modelling to remove the stationarity assumption includes the spatially varying kernel approach of Higdon et al. (1999) and the local stationarity approach of Fuentes & Smith (2001) but both are still within the setting of GP’s. The fundamental paper of Sampson ”
Shared Segmentation of Natural Scenes Using Dependent Pitman-Yor Processes (2008, background): “Several authors have independently proposed an alternative spatial model based on a pointwise, multinomial logistic transformation of K latent GPs [36–39].”