Area-interaction point processes

By Adrian Baddeley, V. Lieshout. 1993

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Determinantal Point Processes for Machine Learning (2012, background): “The Gibbs point process provides such an approach, offering a general framework for incorporating correlations among selected items [Preston, 1976, Ripley and Kelly, 1977, Ripley, 1991, Van Lieshout, 2000, Møller and Waagepetersen, 2004, 2007, Daley and Vere-Jones, 2008].”
Perfect simulation using dominating processes on ordered spaces, with application to locally stable point processes (2000, background): “These conditions are satisfied by most Markov point processes and nearest-neighbour Markov point processes [1,12,30,39] including for example the Strauss process [17,40], the continuum random-cluster model [16,25,29,30], the saturation and triplets processes discussed in [12] and the area-interactio”
Correction note: Poisson point process models solve the “pseudo-absence problem” for presence-only data in ecology (2010, methodology): “One possible method of modeling this clustering is to ﬁt an area-interaction process [Baddeley and van Lieshout (1995)], using the spatstat package.”
Generalised shot noise Cox processes (2005, background): “Two exceptions are the Strauss process [20], [45] and the area-interaction process [3], [50].”
Characterization results and Markov chain Monte Carlo algorithms including exact simulation for some spatial point processes (1999, methodology): “A more promising way of modelling attraction between points in a spatial pattern is to allow interaction terms of higher order as in Baddeley and van Lieshout (1995), Geyer (1999) and Møller (1999), or to generalize the Markov property to depend on the conﬁguration as in the deﬁnition of…”