Point Processes
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Tick: a Python library for statistical learning, with a particular emphasis on time-dependent modelling
Tick is a statistical learning library for Python~3, with a particular emphasis on time-dependent models, such as point processes, and tools for generalized linear models and survival analysis.
DPPy: Sampling DPPs with Python
Determinantal point processes (DPPs) are specific probability distributions over clouds of points that are used as models and computational tools across physics, probability, statistics, and more recently machine learning.
Zonotope hit-and-run for efficient sampling from projection DPPs
Previous theoretical results yield a fast mixing time of our chain when targeting a distribution that is close to a projection DPP, but not a DPP in general.
Determinantal point processes for machine learning
Determinantal point processes (DPPs) are elegant probabilistic models of repulsion that arise in quantum physics and random matrix theory.
PoPPy: A Point Process Toolbox Based on PyTorch
• HongtengXu/PoPPy
• 
In practice, the key points of point process-based sequential data modeling include: 1) How to design intensity functions to describe the mechanism behind observed data?
THAP: A Matlab Toolkit for Learning with Hawkes Processes
• HongtengXu/Hawkes-Process-Toolkit
As a powerful tool of asynchronous event sequence analysis, point processes have been studied for a long time and achieved numerous successes in different fields.
Learning Hawkes Processes from Short Doubly-Censored Event Sequences
ICML 2017 • HongtengXu/Hawkes-Process-Toolkit
Many real-world applications require robust algorithms to learn point processes based on a type of incomplete data --- the so-called short doubly-censored (SDC) event sequences.
A Dirichlet Mixture Model of Hawkes Processes for Event Sequence Clustering
NeurIPS 2017 • HongtengXu/Hawkes-Process-Toolkit
We propose an effective method to solve the event sequence clustering problems based on a novel Dirichlet mixture model of a special but significant type of point processes --- Hawkes process.
Variational Latent Gaussian Process for Recovering Single-Trial Dynamics from Population Spike Trains
In the V1 dataset, we find that vLGP achieves substantially higher performance than previous methods for predicting omitted spike trains, as well as capturing both the toroidal topology of visual stimuli space, and the noise-correlation.
Wasserstein Learning of Deep Generative Point Process Models
NeurIPS 2017
• xiaoshuai09/Wasserstein-Learning-For-Point-Process
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Point processes are becoming very popular in modeling asynchronous sequential data due to their sound mathematical foundation and strength in modeling a variety of real-world phenomena.