mcmc_temporal_contmark: Bayesian Estimation of Temporal Hawkes Model Parameters with...
In stpphawkes: Missing Data for Marked Hawkes Process

mcmc_temporal_contmark

R Documentation

Bayesian Estimation of Temporal Hawkes Model Parameters with Categorical Marks

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_contmark(
  times,
  marks,
  wshape,
  t_max = max(times),
  t_mis = NULL,
  param_init = NULL,
  mcmc_param = NULL,
  branching = TRUE,
  dist = "Weibull",
  print = TRUE
)

Arguments

`times`	- vector of arrival times
`marks`	- vector of continuous marks
`wshape`	- fixed weibull shape parameter
`t_max`	- maximum time value (default = max(times))
`t_mis`	- `M`x`2` 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')
`dist`	- distribution for marks string (default = "Weibull")
`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.

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.