Bayesian Inference for Neyman-Scott Point Processes
Bayesian inference for Neyman-Scott point processes
Bayesian MCMC estimation of parameters of generalized Thomas process
Results for Bayesian MCMC estimation of parameters of generalized Thom...
Estimation of Thomas-type cluster point process with complex inhomogen...
Estimate the first-order inhomogeneity
Graphical output describing the posterior distributions
Text output describing the posterior distributions
Re-estimate the posterior distributions with different burn-in
Simulation of generalized Thomas process
Simulate a realization of Thomas-type cluster point process with compl...
The Bayesian MCMC estimation of parameters for Thomas-type cluster point process with various inhomogeneities. It allows for inhomogeneity in (i) distribution of parent points, (ii) mean number of points in a cluster, (iii) cluster spread. The package also allows for the Bayesian MCMC algorithm for the homogeneous generalized Thomas process. The cluster size is allowed to have a variance that is greater or less than the expected value (cluster sizes are over or under dispersed). Details are described in Dvořák, Remeš, Beránek & Mrkvička (2022) <arXiv: 10.48550/arXiv.2205.07946>.