Spatiotemporal prediction for log‐Gaussian Cox processes

By A. Brix, Peter J. Diggle. 2001

234 citations recorded by the source corpus.

Subfields: Applied Mathematics, Geometry and Topology, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/5a0b20527267bfd758c01197e32b164b2cc29146

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In Applied Mathematics

In Geometry and Topology

In Global and Planetary Change

Citations in the corpus

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).”
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).”
Exact Bayesian inference in spatiotemporal Cox processes driven by multivariate Gaussian processes (2015, methodology): “Similar processes were proposed in continuous time by Brix and Diggle (2001). Several options are available for the temporal transition matrix G, including the identity matrix.”
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).”
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].”
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).”
Shot noise Cox processes (2003, background): “Many recent papers [6]–[10], [12], [19], [34], [48], [49] deal with simulation-based inference for new flexible model classes of such Cox processes; see the surveys [30], [32] and the monograph [33].”