Fitting a log Gaussian Cox process with temporally varying effects – a case study
نویسندگان
چکیده
Integrated nested Laplace approximation (INLA) provides a fast and yet quite exact approach to fitting complex latent Gaussian models which comprise many statistical models in a Bayesian context, including log Gaussian Cox processes. This paper discusses how a joint log Gaussian Cox process model may be fitted to independent replicated point patterns. We illustrate the approach by fitting a model to data on the locations of muskoxen (Ovibos moschatus) herds in Zackenberg valley, Northeast Greenland and by detailing how this model is specified within the R-interface R-INLA. The paper strongly focusses on practical problems involved in the modelling process, including issues of spatial scale, edge effects and prior choice, and finishes with a discussion on models with varying boundary conditions.
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