Likelihood Analysis of Spatial Point Patterns

By Yosihiko Ogata, M. Tanemura. 1984

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Citations in the corpus

Determinantal Point Processes for Machine Learning (2012, background): “A variety of alternative definitions for ψ(·) have been proposed [Ripley and Kelly, 1977, Ogata and Tanemura, 1984].”
Model-Based Multiple Instance Learning (2017, methodology): “Numerical approximation method proposed in [39] and Markov Chain Monte Carlo (MCMC) method proposed in [40] are highly specific to the chosen model, computationally intensive, and require careful tuning to ensure good performance.”
Area-interaction point processes (1993, methodology): “Maximum likelihood estimation therefore rests on numerical or Monte Carlo approximations of c~ ( Ogata and Tanemura (1981, 1984, 1989) , Penttinen (1984)) or recursive approximation methods (Moyeed and Baddeley (1991)).”
Recent applications of point process methods in forestry statistics (2000, methodology): “Other applications can be found in Ogata and Tanemura (1984,1985), Penttinen, Stoyan and Henttonen (1992), Stoyan, Kendall and Mecke (1995) and Sarkka and Tomppo (1998).”
A double Metropolis–Hastings sampler for spatial models with intractable normalizing constants (2010, methodology): “This data set has been analysed by Ripley [31], Ogata and Tanemura [32] and Gu and Zhu [28] using the same model.”
A comparison between parametric and non-parametric approaches to the analysis of replicated spatial point patterns (2000, methodology): “Our third model, previously used by Ogata and Tanemura (1984) and Diggle et al. (1994), has interaction function e(t) = 1 − exp{−(t/θ)2}.”
spatstat: An R Package for Analyzing Spatial Point Patterns (2005, methodology): “Although methods for fitting point process models have been available since the 1970’s Besag (1975); Diggle (2003); Ogata and Tanemura (1981, 1984) ; Moller and Waagepetersen (2003); Ripley (1981, 1988), most of these methods were very specific to the chosen model, and there were no software implem”
Introduction to the Theory of Gibbs Point Processes (2017, methodology): “rameters is the likelihood inference. However a practical issue is that the likelihood depends on the intractable partition function. In the case of sparse data, approximations were ﬁrst pr oposed in [43], before simulation-based methods have been developed [25]. Here, we treat only the theoretical ”
Estimation of Interaction Potentials of Marked Spatial Point Patterns Through the Maximum Likelihood Method (1985, methodology): “However, the genuine hard-core potential can be compared with others by using HIC = AIC + 2 instead of the AIC (Ogata and Tanemura, 1984).”