mcmc_temporal: Bayesian Estimation of Temporal Hawkes Model Parameters

In stpphawkes: Missing Data for Marked Hawkes Process

Bayesian Estimation of Temporal Hawkes Model Parameters

Description

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

Usage

mcmc_temporal(
  times,
  t_max = max(times),
  t_mis = NULL,
  param_init = NULL,
  mcmc_param = NULL,
  branching = TRUE,
  print = TRUE
)

Arguments

times	- vector of arrival times
t_max	- maximum time value (default = max(times))
t_mis	- Mx2 matrix, mth row contains two elements describing the mth 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')

Details

The default is to estimate the branching structure which is much more computationally efficient. The model will also account to missing data if t_mis is provided.

Branching models specify gamma priors for mu, alpha and beta parameters.

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

Examples

times = simulate_temporal(.5,.1,.5,c(0,10),numeric())
out = mcmc_temporal(times)

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