NetworkHawkesProcesses.jl

07/26/2023, 7:00 PM — 7:30 PM UTC
32-123

Abstract:

NetworkHawkesProcesses.jl implements methods to simulate and estimate a class of probabilistic models that combines mutually-exciting Hawkes processes with network structure. It allows researchers to construct such models from a flexible set of model components, run inference from a list of compatible methods (including maximum-likelihood estimation, Markov chain Monte Carlo sampling, and variational inference), and explore results with visualization and diagnostic utilities.

Description:

NetworkHawkesProcesses.jl is a pure Julia framework for defining, simulating, and performing inference on a class of probabilistic models that permit simultaneous inference on the structure of a network and its event generating process—the network Hawkes processes (Linderman, 2016). The event generating process is assumed to follow an auto-regressive, multi-variate Poisson process known as a Hawkes process. Connections between nodes—the network "structure"—are assumed to follow any standard network model (i.e., independent connections). Combining these models provides a disciplined method for discovering latent network structure from event data observed in neuroscience, finance, and beyond.

Package features

  • Supports continuous and discrete processes
  • Uses modular design to support extensible components
  • Implements simulation via Poisson thinning
  • Provides multiple estimation/inference methods
  • Supports a wide range of network specifications
  • Supports non-homogeneous baselines
  • Accelerates methods via Julia's built-in Threads module

Platinum sponsors

JuliaHub

Gold sponsors

ASML

Silver sponsors

Pumas AIQuEra Computing Inc.Relational AIJeffrey Sarnoff

Bronze sponsors

Jolin.ioBeacon BiosignalsMIT CSAILBoeing

Academic partners

NAWA

Local partners

Postmates

Fiscal Sponsor

NumFOCUS