mcmc_stpp {stpphawkes}

Bayesian Estimation of Spatio-Temporal Hawkes Model Parameters

Description

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

Usage

mcmc_stpp(
  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 DataFrame containing the mcmc samples