# Variational-Inference-for-SGP-Modulated-Hawkes-Process **Repository Path**: z2007c/Variational-Inference-for-SGP-Modulated-Hawkes-Process ## Basic Information - **Project Name**: Variational-Inference-for-SGP-Modulated-Hawkes-Process - **Description**: This repo contains a demo for the AAAI-20 publication "Variational Inference for Sparse Gaussian Process Modulated Hawkes Process" - **Primary Language**: Unknown - **License**: MIT - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2020-05-18 - **Last Updated**: 2020-12-19 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # [Variational-Inferencce-for-SGP-Modulated-Hawkes-Process](https://arxiv.org/abs/1905.10496) This repo contains a demo for the AAAI-20 publication "[Variational Inference for Sparse Gaussian Process Modulated Hawkes Process](https://arxiv.org/abs/1905.10496)". It works in python 3.5/3.6. # Required Packages - numpy - scipy==1.2.0 - matplotlib - autograd==1.1.13 - [tick](https://github.com/X-DataInitiative/tick)==0.5.0.0 - numpydoc==0.7.0 They be installed through pip: ```bash $ pip3 install numpy scipy==1.2.0 matplotlib autograd==1.1.13 tick==0.5.0.0 numpydoc==0.7.0 ``` # Citation If you find Variational Inference for Sparse Gaussian Process Modulated Hawkes Process useful in your research, please consider citing: @article{zhang2020variational, title={Variational Inference for Sparse Gaussian Process Modulated Hawkes Process}, author={Zhang, Rui and Walder, Christian and Rizoiu, Marian Andrei}, journal={the 34th AAAI Conference on Artificial Intelligence (AAAI-20)}, year={2020} } # Tutorial See demo.ipynb. # Online Demo An online demo is on [GoogleColab](https://colab.research.google.com/github/RuiZhang2016/Variational-Inference-for-SGP-Modulated-Hawkes-Process/blob/master/demo.ipynb). # License [MIT License](https://github.com/RuiZhang2016/Variational-Inferencce-for-SGP-Modulated-Hawkes-Process/blob/master/LICENSE)