Space‐time Multi Type Log Gaussian Cox Processes with a View to Modelling Weeds

By A. Brix, Jesper Møller. 2001

93 citations recorded by the source corpus.

Subfields: Environmental Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/b9729fd7755452c3ab835cb9e5f793cbff3fd3cc

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In Environmental Engineering

In Statistics and Probability

Citations in the corpus

Generalised shot noise Cox processes (2005, background): “Recently, simulation-based inference for certain families of Cox processes has been studied in great detail [5], [6], [8], [49], [35], [32], [33], [34], [51].”
Mixture Modeling for Marked Poisson Processes (2010, methodology): “A standard approach to (approximate) Bayesian inference for NHPPs is based upon logGaussian Cox process models, wherein the random intensity function is modeled on logarithmic scale as a Gaussian process (e.g., Møller et al., 1998; Brix and Diggle, 2001; Brix and Møller, 2001).”
Handbook of Spatial Statistics (2008, background): “…conditional density which is of fundamental importance in the present chapter), while other kinds of spatio-temporal point process models, which are closely related to the Cox point process models considered in this chapter, can be found in e.g. Brix and Diggle (2001) and Brix and Møller (2001).”
Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations (2009, unknown): “Log-Gaussian Cox processes are a flexible class of models that have been successfully used for modelling spatial or spatiotemporal point processes; see for example Møller et al. (1998), Brix and Møller (2001), Brix and Diggle (2001) and Møller and Waagepetersen (2003).”
Structured Spatio‐Temporal Shot‐Noise Cox Point Process Models, with a View to Modelling Forest Fires (2010, methodology): “For spatio-temporal point pattern data, spatio-temporal logGaussian Cox process models have recently found different applications (Brix and Møller, 2001; Brix and Diggle, 2001, 2003; Brix and Chadoeuf, 2002; Diggle et al., 2005).”
Bayesian mixture modeling for spatial Poisson process intensities, with applications to extreme value analysis (2007, background): “Extensions to spatio-temporal settings are considered in Brix and Diggle (2001) and Brix and Møller (2001).”