Hierarchical log Gaussian Cox process for regeneration in uneven-aged forests

@article{Kuronen2021HierarchicalLG,
  title={Hierarchical log Gaussian Cox process for regeneration in uneven-aged forests},
  author={Mikko Kuronen and Aila S{\"a}rkk{\"a} and Matti Vihola and Mari Myllym{\"a}ki},
  journal={Environmental and Ecological Statistics},
  year={2021},
  volume={29},
  pages={185-205}
}
We propose a hierarchical log Gaussian Cox process (LGCP) for point patterns, where a set of points $$\varvec{x}$$ x affects another set of points $$\varvec{y}$$ y but not vice versa. We use the model to investigate the effect of large trees on the locations of seedlings. In the model, every point in $$\varvec{x}$$ x has a parametric influence kernel or signal, which together form an influence field. Conditionally on the parameters, the influence field acts as a spatial covariate in the… 

References

SHOWING 1-10 OF 49 REFERENCES
Approximate Bayesian Inference for Latent Gaussian Models
TLDR
The approximation tool for latent GMRF models is introduced and the approximation for the posterior of the hyperparameters θ in equation (1) is shown to give extremely accurate results in a fraction of the computing time used by MCMC algorithms.
Weak convergence and optimal scaling of random walk Metropolis algorithms
This paper considers the problem of scaling the proposal distribution of a multidimensional random walk Metropolis algorithm in order to maximize the efficiency of the algorithm. The main result is a
An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach
TLDR
It is shown that, using an approximate stochastic weak solution to (linear) stochastically partial differential equations, some Gaussian fields in the Matérn class can provide an explicit link, for any triangulation of , between GFs and GMRFs, formulated as a basis function representation.
ggplot2
Hamiltonian Monte Carlo using an adjoint-differentiated Laplace approximation: Bayesian inference for latent Gaussian models and beyond
TLDR
A novel adjoint method is derived that propagates the minimal information needed to construct the gradient of the approximate marginal likelihood and yields a scalable method that is orders of magnitude faster than state of the art techniques when the hyperparameters are high dimensional.
Individual-based Methods in Forest Ecology and Management
This chapter outlines the vision and principles of individual-based forest ecology and management. For the last 20–30years there has been a trend in forest ecology and management to interpret the
New insights on the behaviour of alternative types of individual-based tree models for natural forests
Likelihood Inference for Spatial Point Processes: Likelihood and Computation
...
...