A model for clustering

By D. J. Strauss. 1975

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Citations in the corpus

Determinantal Point Processes for Machine Learning (2012, neutral): “Pairwise Markov processes Strauss [139] introduced a simple pairwise Markov point process for spatial data in which the potential function ψ2(i, j) is piecewise constant, taking the value 1 whenever i and j are at least a fixed radius apart, and the constant value γ otherwise.”
Statistical Inference and Simulation for Spatial Point Processes (2003, unknown): “1 (Strauss process) The simplest nontrivial pairwise interaction process is the Strauss process (Strauss 1975), with φ2(r) = γ1[r≤R] (6.”
spatstat: An R Package for Analyzing Spatial Point Patterns (2005, methodology): “For example, the classical redwood dataset of Figure 1 is a subset extracted by Ripley (1976, 1981) from a larger dataset of Strauss (1975) which is shown in Figure 3.”
An efficient Markov chain Monte Carlo method for distributions with intractable normalising constants (2006, methodology): “We restrict attention to a Strauss point process (Strauss, 1975; Kelly and Ripley, 1976).”
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”
On parameter estimation for pairwise interaction point processes (1994, methodology): “Our first model, due to Strauss (1975), has interaction function”
Estimation of Interaction Potentials of Marked Spatial Point Patterns Through the Maximum Likelihood Method (1985, methodology): “The model PF3 is called Lennard-Jones potential in case of m = 6 and the model PF4 is the Strauss potential (Strauss, 1975), which includes the hardcore potential (T = 0).”
Likelihood-based inference for Matérn type-III repulsive point processes (2009, methodology): “Various methods have been introduced to model repulsive or underdispersed point processes, including simple inhibition processes [17, Chapter 3], thinned processes [3], [27], the Strauss process [31], and Markov point processes [23], which model the configuration by assigning a density (to discourag”
Point process limits of lattice processes (1982, methodology): “As an example, when 'I' is of the form (2.2) and { p u ~ do, h(u) = 1 u > do, where do>°and p E [0, 1), we obtain as a limit P the Strauss process (Strauss (1975), Kelly and Ripley (1976» with density (3.10) f(x) = f(0)A n(x)p'~(X), where s (x) denotes the number of pairs of distinct points that…”
Characterization results and Markov chain Monte Carlo algorithms including exact simulation for some spatial point processes (1999, background): “Strauss (1975) and Kelly and Ripley (1976) considered the Strauss process where Æ is the normalizing constant, (cid:226) .”