https://github.com/elizavetasemenova/prob-epi
Course materials of "Bayesian Modelling and Probabilistic Programming with Numpyro, and Deep Generative Surrogates for Epidemiology"
https://github.com/elizavetasemenova/prob-epi
agent-based-modeling bayesian-inference deep-generative-modelling disease-transmission epidemiology gaussian-processes numpyro probabilistic-programming spatial-statistics surrogates
Last synced: 8 months ago
JSON representation
Course materials of "Bayesian Modelling and Probabilistic Programming with Numpyro, and Deep Generative Surrogates for Epidemiology"
- Host: GitHub
- URL: https://github.com/elizavetasemenova/prob-epi
- Owner: elizavetasemenova
- License: mit
- Created: 2024-01-27T09:32:06.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2025-02-25T13:28:30.000Z (9 months ago)
- Last Synced: 2025-03-31T05:02:51.479Z (8 months ago)
- Topics: agent-based-modeling, bayesian-inference, deep-generative-modelling, disease-transmission, epidemiology, gaussian-processes, numpyro, probabilistic-programming, spatial-statistics, surrogates
- Language: Jupyter Notebook
- Homepage: https://elizavetasemenova.github.io/prob-epi
- Size: 24.6 MB
- Stars: 61
- Watchers: 6
- Forks: 24
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Citation: CITATION.cff
Awesome Lists containing this project
README
https://elizavetasemenova.github.io/prob-epi
## Giving feedback
- To correct typos, please make pull requests on [GitHub](https://github.com/elizavetasemenova/prob-epi). If these notes ever get published, I will list your name in Acknowledgements.
- For more substantial suggestions about the course content, such as desired topics, please use issues on [GitHub](https://github.com/elizavetasemenova/prob-epi) or email them to `elizaveta [dot] p [dot] [insert my surname] [at] gmail [dot] com`.
- If you enjoyed the content **and / or** learnt from it, please leave a 'star' to the [book's GitHub](https://github.com/elizavetasemenova/prob-epi) repository.
- If you are creating a written document (a paper, report, book chapter) where you use what you've learnt here, please cite
```
@software{Semenova_Bayesian_Modelling_and_2024,
author = {Semenova, Elizaveta},
doi = {10.5281/zenodo.11550659},
month = jun,
title = {{Bayesian Modelling and Probabilistic Programming with Numpyro, and Deep Generative Surrogates for Epidemiology.}},
url = {https://github.com/elizavetasemenova/prob-epi},
version = {v1.0.0},
year = {2024}
}
```
## Conda environment
To run the code examples from the course, the recommended Conda environemnt can be created as follows:
```
conda create -n aims python=3.9
conda activate aims
conda install -c conda-forge jupyter-book
conda install conda-forge::numpyro
conda install conda-forge::jax
pip install -U jax
pip install flax
conda install conda-forge::matplotlib
conda install numpy
conda install conda-forge::ghp-import
pip install sphinxcontrib-tikz
conda install conda-forge::geopandas
conda install conda-forge::arviz
conda install anaconda::seaborn
pip install pyppeteer
```