Case Studies in Spatial Point Process Modeling

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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].”
Global envelope tests for spatial processes (2013, background): “…spatial ecology (e.g. Wiegand and Moloney (2014)), biological sciences (e.g. Schladitz et al. (2003), Mattfeldt et al. (2006, 2009) and Weston et al. (2012)), geography (e.g. Cliff and Ord (1983)) and astronomy (e.g. Vio et al. (2007) and Martı́nez et al. (2010)); also see Baddeley et al. (2006).”
Multiobjective evolutionary algorithms to identify highly autocorrelated areas: the case of spatial distribution in financially compromised farms (2014, background): “…of spatial units (SU) where a phenomenon is significantly concentrated is specially relevant in many decisional situations both to identify the causes of such spatial aggregation and to support decision making (Baddeley et al. 2006; Andrienko and Andrienko 2006; Gutiérrez et al. 2008; Noyan 2010).”