Python framework for inference in Hawkes processes. PyHawkes implements a variety of Bayesian inference algorithms for discovering latent network structure given point process observations. Suppose you observe timestamps of Twitter messages, but you don’t get to see how those users are connected to one another. You might infer that there is an unobserved connection from one user to another if the first user’s activity tends to precede the second user’s. This intuition is formalized by combining excitatory point processes (aka Hawkes processes) with random network models and performing Bayesian inference to discover the latent network.
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References in zbMATH (referenced in 7 articles )
Showing results 1 to 7 of 7.
- Holbrook, Andrew J.; Loeffler, Charles E.; Flaxman, Seth R.; Suchard, Marc A.: Scalable Bayesian inference for self-excitatory stochastic processes applied to big American gunfire data (2021)
- Bacry, Emmanuel; Bompaire, Martin; Gaïffas, Stéphane; Muzy, Jean-Francois: Sparse and low-rank multivariate Hawkes processes (2020)
- Price-Williams, Matthew; Heard, Nicholas A.: Nonparametric self-exciting models for computer network traffic (2020)
- Metelli, Silvia; Heard, Nicholas: On Bayesian new edge prediction and anomaly detection in computer networks (2019)
- Emmanuel Bacry, Martin Bompaire, Stéphane Gaïffas, Soren Poulsen: Tick: a Python library for statistical learning, with a particular emphasis on time-dependent modelling (2017) arXiv
- Hongteng Xu, Hongyuan Zha: THAP: A Matlab Toolkit for Learning with Hawkes Processes (2017) arXiv
- Bacry, Emmanuel; Gaïffas, Stéphane; Mastromatteo, Iacopo; Muzy, Jean-François: Mean-field inference of Hawkes point processes (2016)