mcmc_stpp_nonunif: Bayesian Estimation of Spatio-Temporal Hawkes Model...

In stpphawkes: Missing Data for Marked Hawkes Process

Bayesian Estimation of Spatio-Temporal Hawkes Model Parameters with non uniform spatial locations

Description

This function computes the posterior of a spatio-temporal exponential decay Hawkes model using Metropolis-with-in-Gibbs sampling.

Usage

mcmc_stpp_nonunif(
  data,
  poly,
  t_max = max(data$t),
  t_mis = NULL,
  param_init = NULL,
  mcmc_param = NULL,
  branching = TRUE,
  print = TRUE,
  sp_clip = TRUE
)

Arguments

data	- A DataFrame containing x,y,t
poly	- matrix defining polygon (N x 2)
t_max	- maximum time value (default = max(times))
t_mis	- vector of two elements describing missing time range (default = 'NULL')
param_init	- list of parameters of initial guess (default = 'NULL', will start with MLE)
mcmc_param	- list of mcmc parameters
branching	- using branching structure in estimation (default = 'TRUE')
print	- print progress (default = 'TRUE')
sp_clip	- when simulating missing data spatial points, clip spatial region back to observed region (default = 'TRUE')

Details

The default is to estimate the branching structure. The model will also account to missing data if t_mis is provided.

Value

A List containing the mcmc samples (samps), branching structure ('y', if 'TRUE'), and missing data ('zsamps' if 't_mis' is not 'NULL') If 't_mis' is not 'NULL' the mcmc samples will contain 'n_missing', the number of missing points estimated

stpphawkes documentation built on April 4, 2025, 3:22 a.m.