Bayesian Inference for Neyman-Scott Point Processes
Generate auxiliary variable for given proposed parameters.
binspp: Bayesian Inference for Neyman-Scott Point Processes
Bayesian inference for Neyman-Scott point processes
Calculate parameters for Birth and Death Interaction likelihood functi...
Auxiliary function which calculates sum values Bayesian MCMC estimatio...
Results for Bayesian MCMC estimation of parameters of generalized Thom...
Estimation of interaction Neyman-Scott point process using auxiliary v...
Estimation of Thomas-type cluster point process with complex inhomogen...
Estimate the first-order inhomogeneity
Evaluate unnormalized likelihood for auxiliary variable
plot_conn
Graphical output describing the posterior distributions
Text output describing the posterior distributions
Text output describing the posterior distributions
Obtaining the raw MCMC output
Re-estimate the posterior distributions with a different burn-in or a ...
Simulation of generalized Thomas process
Simulate a realization of Thomas-type cluster point process with compl...
Simulation from the fitted model
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>.