https://github.com/lacerbi/padova2022-bayes

Tutorial on Bayesian model fitting with PyVBMC.
https://github.com/lacerbi/padova2022-bayes

Last synced: 4 months ago
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Tutorial on Bayesian model fitting with PyVBMC.

• Host: GitHub
• URL: https://github.com/lacerbi/padova2022-bayes
• Owner: lacerbi
• License: mit
• Created: 2022-12-16T22:38:48.000Z (almost 3 years ago)
• Default Branch: main
• Last Pushed: 2022-12-17T09:28:33.000Z (almost 3 years ago)
• Last Synced: 2025-03-30T20:48:16.654Z (7 months ago)
• Language: Jupyter Notebook
• Size: 13 MB
• Stars: 2
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Practical and efficient Bayesian model fitting with Variational Bayesian Monte Carlo (PyVBMC)

Tutorial on Bayesian model fitting using the PyVBMC package, presented at the *Data Science in Action* event at the University of Padova (December 2022).

**Lecturer:** [Luigi Acerbi](https://www.helsinki.fi/en/researchgroups/machine-and-human-intelligence), [@AcerbiLuigi](https://twitter.com/AcerbiLuigi) (University of Helsinki).

- To run the tutorial, download / clone the repository locally.

- Ensure that the [PyVBMC package](https://github.com/acerbilab/pyvbmc) is installed.

- **Notebook:** [acerbi_bayes_tutorial.ipynb](acerbi_bayes_tutorial.ipynb)

- **Slides:** [acerbi-bayes-padova-dec2022.pdf](acerbi-bayes-padova-dec2022.pdf)

![PyVBMC demo](vbmc_animation.gif)

### License

Unless stated otherwise, the material in this repo is released under the [MIT License](LICENSE).