https://github.com/choderalab/fortuna

Adaptive sampling methods for optimising simulations
https://github.com/choderalab/fortuna

adaptive-sampling bayesian-bandits

Last synced: 4 months ago
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Adaptive sampling methods for optimising simulations

• Host: GitHub
• URL: https://github.com/choderalab/fortuna
• Owner: choderalab
• License: mit
• Created: 2019-05-31T15:17:24.000Z (about 7 years ago)
• Default Branch: master
• Last Pushed: 2021-09-20T20:37:37.000Z (over 4 years ago)
• Last Synced: 2025-09-10T02:31:48.660Z (9 months ago)
• Topics: adaptive-sampling, bayesian-bandits
• Language: Jupyter Notebook
• Homepage:
• Size: 196 KB
• Stars: 1
• Watchers: 8
• Forks: 2
• Open Issues: 5
• Metadata Files:
  • Readme: README.md
  • Contributing: .github/CONTRIBUTING.md
  • License: LICENSE
  • Code of conduct: CODE_OF_CONDUCT.md

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# FORTUNA

Methods for adaptive sampling.

* Bayesian bandits

* Optimal experimental design

## License

This software is licensed under the [MIT license](https://opensource.org/licenses/MIT), a permissive open source license.

## Notice

Please be aware that this code is made available in the spirit of open science, but is currently pre-alpha--that is,

**it is not guaranteed to be completely tested or provide the correct results**, and the API can change at any time

without warning. If you do use this code, do so at your own risk. We appreciate your input, including raising issues

about potential problems with the code, but may not be able to address your issue until other development activities

have concluded.

## Authors

* Hannah E. Bruce Macdonald

* Dominic A. Rufa

* John D. Chodera

#### Acknowledgements

 

Project based on the 

[Computational Molecular Science Python Cookiecutter](https://github.com/molssi/cookiecutter-cms) version 1.0.

This project is supported by [MolSSI](https://molssi.org).