Skip to main content

A pip installable version of the lognormal mixture distribution from https://github.com/shchur/ifl-tpp/tree/master/code

Project description

Intensity-Free Learning of Temporal Point Processes

Pip installable version of the pytorch lognormal mixture distribution from the paper "Intensity-Free Learning of Temporal Point Processes", Oleksandr Shchur, Marin Biloš and Stephan Günnemann, ICLR 2020.

Requirements

pytorch>=1.2.0

Cite

Please cite the original author's paper if you use this code in your own work

@article{
    shchur2020intensity,
    title={Intensity-Free Learning of Temporal Point Processes},
    author={Oleksandr Shchur and Marin Bilo\v{s} and Stephan G\"{u}nnemann},
    journal={International Conference on Learning Representations (ICLR)},
    year={2020},
}

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

pytorch_lognormal_mixture-0.0.1.tar.gz (5.1 kB view hashes)

Uploaded Source

Built Distribution

pytorch_lognormal_mixture-0.0.1-py3-none-any.whl (6.6 kB view hashes)

Uploaded Python 3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page