MCMC Inference for a Hawkes process in Julia
Author dm13450
8 Stars
Updated Last
1 Year Ago
Started In
April 2018

Coverage Status


A Julia package for fitting, analysing and plotting Hawkes processes.

You can find the maths behind the algorithm here. The Bayesian sampling algorithm is a generic version of the bayesianETAS package from CRAN which can be found here.



I provide a number of different tools to both fit and analyse a collection of events using Hawkes processes.

Simulate a Hawkes process

bg = 0.5
kappa = 0.5
kernel(x) = pdf.(Distributions.Exponential(1/0.5), x)
maxT = 100
simevents = HawkesProcesses.simulate(bg, kappa, kernel, maxT)

Enhanced Bayesian Inference

Sample the parameters of a Hawkes process using the latent variable Bayesian MCMC algorithm. Currently only constant background, constant κ and exponential kernel are available, but this will be extend to generic functions in the future.

bgSamps, kappaSamps, kernSamps = HawkesProcesses.fit(simEvents, maxT, 1000)

Calculate the likelihood for a Hawkes process

likelihood = HawkesProcesses.likelihood(simevents, bg, kappa, Distributions.Exponential(1/0.5))

Intensity calculation

intensity = HawkesProcesses.intensity(ts, simevents, bg, kappa, kernel)

To Do

  • Make functions consistent with Julia style guide.
  • Check type safety
  • Return parent vector from simulate.

Next Features

  • Likelihood with functional background
  • Generic Bayesian inference.

Used By Packages

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